This notebook analyses both parts of the data in terms of variable importance, using a random forest model based on conditional inference trees and a conditional permutation variable importance algorithm.

Setup

Load packages

# load packages
library(tidyr)
library(ggplot2)
library(party)
library(conflicted)
library(tidyverse)
library(openxlsx)
library(caret)
library(viridis)
library(cowplot)
library(permimp)
# set package parameters
theme_set(theme_bw())

# plot colour scheme

mycolourlist = list(c(0, 102, 255), c(0, 204, 153), c(255, 0, 102), c(74, 111, 152), c(251, 164, 49), c(204, 153, 255), c(90, 192, 255), c(80, 245, 233), c(255, 90, 192), c(164, 201, 242), c(255, 254, 139), c(255, 243, 255))
mycolours = matrix()

for (ii in 1:length(mycolourlist)){
  mycolours[ii] = rgb(mycolourlist[[ii]][1]/255,
                      mycolourlist[[ii]][2]/255,
                      mycolourlist[[ii]][3]/255)
}

# toggle to save plots
saveplots = TRUE

if (saveplots){
  # set output plot directory
  choose.files(caption="Just cancel this", filters=matrix(data=c(" ", " "), ncol=2))  # workaround for bug in RTerm choose.dir
  outFigPath <- utils::choose.dir(caption="Select output folder to save plots '03 Experiment\\Experiment 1\\Analysis\\Plots'")
  
  if (!dir.exists(file.path(outFigPath, "svg"))){dir.create(file.path(outFigPath, "svg"))}
  if (!dir.exists(file.path(outFigPath, "pdf"))){dir.create(file.path(outFigPath, "pdf"))}
  
}

# toggle to save data
savedata = TRUE

if (savedata){
  # set output plot directory
  if (saveplots==FALSE){
    choose.files(caption="Just cancel this", filters=matrix(data=c(" ", " "), ncol=2))  # workaround for bug in RTerm choose.dir
  }
  outDataPath <- utils::choose.dir(caption="Select output folder to save data '03 Experiment\\Experiment 1\\Analysis\\R'")
}
 

Import data and wrangle


stimDatapath <- utils::choose.files(caption=r"(Select refmap_listest1_testdata_ByStim.csv from 03 Experiment\Experiment 1\Analysis\PostProcess)",
                                     filters=matrix(data=c("refmap_listest1_testdata_ByStim.csv", "refmap_listest1_testdata_ByStim.csv"), ncol=2))

stimData <- utils::read.csv(stimDatapath, header=TRUE)

colnames(stimData)[1] <- "Stimulus"

# make response proportions into percentages
stimData[['HighAnnoyPc']] <- stimData[['HighAnnoyProp']]*100
stimData[['dHighAnnoyPc']] <- stimData[['dHighAnnoyProp']]*100
# function to encode categorical to ordinal numeric variables
encode_ordinal <- function(x, order=unique(x)) {
  x <- as.numeric(factor(x, levels=order, exclude=NULL, order=TRUE))
  x
}

# definition of ordinal variable levels
SNRCats <- c("No UAS", "-16", "-10", "-4", "2", "8")
UASLAeqCats <- c("No UAS", "42", "48", "54", "60")

The aggregated data by stimulus are assigned to a dataframe, relevant categorical variables are converted to ordinal, and then the variable subset of interest is selected, NA rows dropped (ie, the ‘no UAS’ stimuli, as the conditional variable importance algorithm cannot currently handle NA values, which are present in all the UAS dB metrics), and a formula assigned.


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```r

stimDataNum <- data.frame()

stimDataNum <- cbind(stimData[, 'Stimulus'],
                     stimData[, which(colnames(stimData)==\UASEvents\):
                                which(colnames(stimData)==\UASEventPerMin\)],
                    stimData[, which(colnames(stimData)==\UASLAeq\):
                                which(colnames(stimData)==\SNRlevel\)],
                    stimData[, which(colnames(stimData)==\IntermitRatioC2MaxLR\):
                                which(colnames(stimData)==\IntermitRatioC5MaxLR\)],
                    stimData[, which(colnames(stimData)==\UASLAeqMaxLR\):
                                which(colnames(stimData)==\UASEPNLMaxLR\)],
                    stimData[, which(colnames(stimData)==\UASLoudECMAPowAvgBin\):
                                which(colnames(stimData)==\UASImpulsSHM05ExMaxLR\)],
                    stimData[, which(colnames(stimData)==\LAeqLAF90diff\):
                                which(colnames(stimData)==\dImpulsSHM05ExMaxLR\)],
                    stimData[, which(colnames(stimData)==\ValenceMedian\):
                               which(colnames(stimData)==\dHighAnnoyProp\)],
                    stimData[, which(colnames(stimData)==\HighAnnoyPc\):
                                which(colnames(stimData)==\dHighAnnoyPc\)])

# remove duplicated variables
stimDataNum <- subset(stimDataNum, select = -c(UASLAeq))

colnames(stimDataNum)[1] <- \Stimulus\

# make the discrete ordinal outcome variables factors
stimDataNum[['UASEvents']] <- factor(stimDataNum[['UASEvents']], levels=c(0, 1, 3, 5, 9), order=TRUE)
stimDataNum[['ValenceMedian']] <- factor(stimDataNum[['ValenceMedian']], levels=c(1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5), order=TRUE)
stimDataNum[['ArousalMedian']] <- factor(stimDataNum[['ArousalMedian']], levels=c(1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5), order=TRUE)
stimDataNum[['AnnoyMedian']] <- factor(stimDataNum[['AnnoyMedian']], levels=c(0, 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5,
                                                                                5.5, 6, 6.5, 7, 7.5, 8, 8.5, 9, 9.5, 10), order=TRUE)
stimDataNum[['dValenceMedian']] <- factor(stimDataNum[['dValenceMedian']], levels=c(-4, -3.5, -3, -2.5, -2, -1.5, -1, -0.5, 0,
                                                                                      0.5, 1, 1.5, 2, 2.5, 3, 3.5,  4), order=TRUE)
stimDataNum[['dArousalMedian']] <- factor(stimDataNum[['dArousalMedian']], levels=c(-4, -3.5, -3, -2.5, -2, -1.5, -1, -0.5, 0,
                                                                                      0.5, 1, 1.5, 2, 2.5, 3, 3.5,  4), order=TRUE)
stimDataNum[['dAnnoyMedian']] <- factor(stimDataNum[['dAnnoyMedian']], levels=c(-10, -9.5, -9, -8.5, -8, -7.5, -7, -6.5, -6, -5.5, -5,
                                                                                  -4.5, -4, -3.5, -3, -2.5, -2, -1.5, -1, -0.5,
                                                                                 0, 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5,
                                                                                 5.5, 6, 6.5, 7, 7.5, 8, 8.5, 9, 9.5, 10), order=TRUE)

# omit ambient-only stimuli
stimDataNum <- stimDataNum |> dplyr::filter(UASEvents != 0)


stimDataNum$SNRlevel <- as.numeric(stimDataNum$SNRlevel)
```

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```r

stimDataNum <- data.frame()

stimDataNum <- cbind(stimData[, 'Stimulus'],
                     stimData[, which(colnames(stimData)==\UASEvents\):
                                which(colnames(stimData)==\UASEventPerMin\)],
                    stimData[, which(colnames(stimData)==\UASLAeq\):
                                which(colnames(stimData)==\SNRlevel\)],
                    stimData[, which(colnames(stimData)==\IntermitRatioC2MaxLR\):
                                which(colnames(stimData)==\IntermitRatioC5MaxLR\)],
                    stimData[, which(colnames(stimData)==\UASLAeqMaxLR\):
                                which(colnames(stimData)==\UASEPNLMaxLR\)],
                    stimData[, which(colnames(stimData)==\UASLoudECMAPowAvgBin\):
                                which(colnames(stimData)==\UASImpulsSHM05ExMaxLR\)],
                    stimData[, which(colnames(stimData)==\LAeqLAF90diff\):
                                which(colnames(stimData)==\dImpulsSHM05ExMaxLR\)],
                    stimData[, which(colnames(stimData)==\ValenceMedian\):
                               which(colnames(stimData)==\dHighAnnoyProp\)],
                    stimData[, which(colnames(stimData)==\HighAnnoyPc\):
                                which(colnames(stimData)==\dHighAnnoyPc\)])

# remove duplicated variables
stimDataNum <- subset(stimDataNum, select = -c(UASLAeq))

colnames(stimDataNum)[1] <- \Stimulus\

# make the discrete ordinal outcome variables factors
stimDataNum[['UASEvents']] <- factor(stimDataNum[['UASEvents']], levels=c(0, 1, 3, 5, 9), order=TRUE)
stimDataNum[['ValenceMedian']] <- factor(stimDataNum[['ValenceMedian']], levels=c(1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5), order=TRUE)
stimDataNum[['ArousalMedian']] <- factor(stimDataNum[['ArousalMedian']], levels=c(1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5), order=TRUE)
stimDataNum[['AnnoyMedian']] <- factor(stimDataNum[['AnnoyMedian']], levels=c(0, 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5,
                                                                                5.5, 6, 6.5, 7, 7.5, 8, 8.5, 9, 9.5, 10), order=TRUE)
stimDataNum[['dValenceMedian']] <- factor(stimDataNum[['dValenceMedian']], levels=c(-4, -3.5, -3, -2.5, -2, -1.5, -1, -0.5, 0,
                                                                                      0.5, 1, 1.5, 2, 2.5, 3, 3.5,  4), order=TRUE)
stimDataNum[['dArousalMedian']] <- factor(stimDataNum[['dArousalMedian']], levels=c(-4, -3.5, -3, -2.5, -2, -1.5, -1, -0.5, 0,
                                                                                      0.5, 1, 1.5, 2, 2.5, 3, 3.5,  4), order=TRUE)
stimDataNum[['dAnnoyMedian']] <- factor(stimDataNum[['dAnnoyMedian']], levels=c(-10, -9.5, -9, -8.5, -8, -7.5, -7, -6.5, -6, -5.5, -5,
                                                                                  -4.5, -4, -3.5, -3, -2.5, -2, -1.5, -1, -0.5,
                                                                                 0, 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5,
                                                                                 5.5, 6, 6.5, 7, 7.5, 8, 8.5, 9, 9.5, 10), order=TRUE)

# omit ambient-only stimuli
stimDataNum <- stimDataNum |> dplyr::filter(UASEvents != 0)


stimDataNum$SNRlevel <- as.numeric(stimDataNum$SNRlevel)

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# Random forest functions

Write a function to train a conditional-inference random forest (crf) model on input data according to input formula, iterate over input random seeds, average error and variable importance metrics, and output metrics with plotted

## Averaging over multiple random seeds


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initialise variables\n  crfOOBErrAll <- 0\n  crfOOBRMSE <- 0\n  crfOOBMAE <- 0\n  crfOOBErrR2 <- 0\n  crfMarPermImpVals <- 0\n  crfConPermImpVals <- 0\n  crfMarPermImpValsPerTree <- data.frame()\n  crfConPermImpValsPerTree <- data.frame()\n  \n  for (iters in 1:length(seeds)){\n    \n    # formula for regression\n    formVars <- reformulate(iVars, dVar)\n    \n    # set random seed\n    set.seed(seeds[iters])\n    # train crf model\n    crfModel <- party::cforest(formVars, data=dataIn,\n                               controls=party::cforest_unbiased(ntree=ntree,\n                                                                mtry=mtry,\n                                                                minsplit=minsplit,\n                                                                minbucket=minbucket))\n    \n    # get OOB predictions\n    crfModelOOB <- predict(crfModel, OOB=TRUE, type='response')\n    \n    # get OOB error\n    crfModelOOBErr <- as.numeric(as.matrix(as.numeric(as.matrix(crfModelOOB))\n                                            - as.numeric(as.matrix(crfModel@data@env$response[[names(crfModel@data@env$response)]]))))\n\n    # OOB RMSE, error quartiles and Rsquared\n    crfOOBRMSE <- crfOOBRMSE + sqrt(mean(crfModelOOBErr^2))\n    crfOOBMAE <- crfOOBMAE + mean(abs(crfModelOOBErr))\n    crfOOBErrR2 <- crfOOBErrR2 + cor(as.numeric(as.matrix(crfModelOOB)),\n                                     as.numeric(as.matrix(crfModel@data@env$response[[names(crfModel@data@env$response)]])))^2\n\n    # set random seed\n    set.seed(seeds[iters])\n\n    # set random seed\n    set.seed(seeds[iters])\n    # conditional variable permutation importance\n    crfConPermImp <- permimp::permimp(crfModel, nperm=nperm, conditional=TRUE, threshold=permImpCondThres, progressBar=FALSE)\n    \n    crfConPermImpVals <- crfConPermImpVals + crfConPermImp$values\n    crfConPermImpValsPerTree <- rbind(crfConPermImpValsPerTree, crfConPermImp$perTree)\n  }\n  \n  # average metrics\n  crfOOBErrAll <- crfOOBErrAll/length(seeds)\n  crfOOBRMSE <- crfOOBRMSE/length(seeds)\n  crfOOBMAE <- crfOOBMAE/length(seeds)\n  crfOOBErrR2 <- crfOOBErrR2/length(seeds)\n  crfConPermImpVals <- data.frame(CondPermImp=sort(crfConPermImpVals/length(seeds), decreasing=TRUE))\n  #crfConPermImpValsMn <- sort(colMeans(crfConPermImpValsPerTree), decreasing=TRUE)  # mean of conditional variable importance values per tree (same as crfConPermImpVals)\n  crfConPermImpValsQtl <- data.frame(apply(crfConPermImpValsPerTree, 2, quantile, probs=c(0.25, 0.50, 0.75)))\n  \n  resultsOut <- list('OOB_RMSE'=crfOOBRMSE, 'OOB_MAE'=crfOOBMAE, 'Rsquared'=crfOOBErrR2, 'conditional_permimp'=crfConPermImpVals,                      'conditional_permimp_perTree'=crfConPermImpValsPerTree, 'conditional_permimp_qtl'=crfConPermImpValsQtl)\n  return(resultsOut)\n}\n\n```\n\n<!-- rnb-source-end -->\n"} -->

multi_crfReg <- function(dataIn, iVars, dVar, seeds, ntree, mtry, permImpCondThres=0.95, minsplit=20, minbucket=7, nperm=1){
  # initialise variables
  crfOOBErrAll <- 0
  crfOOBRMSE <- 0
  crfOOBMAE <- 0
  crfOOBErrR2 <- 0
  crfMarPermImpVals <- 0
  crfConPermImpVals <- 0
  crfMarPermImpValsPerTree <- data.frame()
  crfConPermImpValsPerTree <- data.frame()
  
  for (iters in 1:length(seeds)){
    
    # formula for regression
    formVars <- reformulate(iVars, dVar)
    
    # set random seed
    set.seed(seeds[iters])
    # train crf model
    crfModel <- party::cforest(formVars, data=dataIn,
                               controls=party::cforest_unbiased(ntree=ntree,
                                                                mtry=mtry,
                                                                minsplit=minsplit,
                                                                minbucket=minbucket))
    
    # get OOB predictions
    crfModelOOB <- predict(crfModel, OOB=TRUE, type='response')
    
    # get OOB error
    crfModelOOBErr <- as.numeric(as.matrix(as.numeric(as.matrix(crfModelOOB))
                                            - as.numeric(as.matrix(crfModel@data@env$response[[names(crfModel@data@env$response)]]))))

    # OOB RMSE, error quartiles and Rsquared
    crfOOBRMSE <- crfOOBRMSE + sqrt(mean(crfModelOOBErr^2))
    crfOOBMAE <- crfOOBMAE + mean(abs(crfModelOOBErr))
    crfOOBErrR2 <- crfOOBErrR2 + cor(as.numeric(as.matrix(crfModelOOB)),
                                     as.numeric(as.matrix(crfModel@data@env$response[[names(crfModel@data@env$response)]])))^2

    # set random seed
    set.seed(seeds[iters])

    # set random seed
    set.seed(seeds[iters])
    # conditional variable permutation importance
    crfConPermImp <- permimp::permimp(crfModel, nperm=nperm, conditional=TRUE, threshold=permImpCondThres, progressBar=FALSE)
    
    crfConPermImpVals <- crfConPermImpVals + crfConPermImp$values
    crfConPermImpValsPerTree <- rbind(crfConPermImpValsPerTree, crfConPermImp$perTree)
  }
  
  # average metrics
  crfOOBErrAll <- crfOOBErrAll/length(seeds)
  crfOOBRMSE <- crfOOBRMSE/length(seeds)
  crfOOBMAE <- crfOOBMAE/length(seeds)
  crfOOBErrR2 <- crfOOBErrR2/length(seeds)
  crfConPermImpVals <- data.frame(CondPermImp=sort(crfConPermImpVals/length(seeds), decreasing=TRUE))
  #crfConPermImpValsMn <- sort(colMeans(crfConPermImpValsPerTree), decreasing=TRUE)  # mean of conditional variable importance values per tree (same as crfConPermImpVals)
  crfConPermImpValsQtl <- data.frame(apply(crfConPermImpValsPerTree, 2, quantile, probs=c(0.25, 0.50, 0.75)))
  
  resultsOut <- list('OOB_RMSE'=crfOOBRMSE, 'OOB_MAE'=crfOOBMAE, 'Rsquared'=crfOOBErrR2, 'conditional_permimp'=crfConPermImpVals,                      'conditional_permimp_perTree'=crfConPermImpValsPerTree, 'conditional_permimp_qtl'=crfConPermImpValsQtl)
  return(resultsOut)
}



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<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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 -->\n\n```r\n```r\n\nmulti_crfReg <- function(dataIn, iVars, dVar, seeds, ntree, mtry, permImpCondThres=0.95, minsplit=20, minbucket=7, nperm=1){\n  # initialise variables\n  crfOOBErrAll <- 0\n  crfOOBRMSE <- 0\n  crfOOBMAE <- 0\n  crfOOBErrR2 <- 0\n  crfMarPermImpVals <- 0\n  crfConPermImpVals <- 0\n  crfMarPermImpValsPerTree <- data.frame()\n  crfConPermImpValsPerTree <- data.frame()\n  \n  for (iters in 1:length(seeds)){\n    \n    # formula for regression\n    formVars <- reformulate(iVars, dVar)\n    \n    # set random seed\n    set.seed(seeds[iters])\n    # train crf model\n    crfModel <- party::cforest(formVars, data=dataIn,\n                               controls=party::cforest_unbiased(ntree=ntree,\n                                                                mtry=mtry,\n                                                                minsplit=minsplit,\n                                                                minbucket=minbucket))\n    \n    # get OOB predictions\n    crfModelOOB <- predict(crfModel, OOB=TRUE, type='response')\n    \n    # get OOB error\n    crfModelOOBErr <- as.numeric(as.matrix(as.numeric(as.matrix(crfModelOOB))\n                                            - as.numeric(as.matrix(crfModel@data@env$response[[names(crfModel@data@env$response)]]))))\n\n    # OOB RMSE, error quartiles and Rsquared\n    crfOOBRMSE <- crfOOBRMSE + sqrt(mean(crfModelOOBErr^2))\n    crfOOBMAE <- crfOOBMAE + mean(abs(crfModelOOBErr))\n    crfOOBErrR2 <- crfOOBErrR2 + cor(as.numeric(as.matrix(crfModelOOB)),\n                                     as.numeric(as.matrix(crfModel@data@env$response[[names(crfModel@data@env$response)]])))^2\n\n    # set random seed\n    set.seed(seeds[iters])\n\n    # set random seed\n    set.seed(seeds[iters])\n    # conditional variable permutation importance\n    crfConPermImp <- permimp::permimp(crfModel, nperm=nperm, conditional=TRUE, threshold=permImpCondThres, progressBar=FALSE)\n    \n    crfConPermImpVals <- crfConPermImpVals + crfConPermImp$values\n    crfConPermImpValsPerTree <- rbind(crfConPermImpValsPerTree, crfConPermImp$perTree)\n  }\n  \n  # average metrics\n  crfOOBErrAll <- crfOOBErrAll/length(seeds)\n  crfOOBRMSE <- crfOOBRMSE/length(seeds)\n  crfOOBMAE <- crfOOBMAE/length(seeds)\n  crfOOBErrR2 <- crfOOBErrR2/length(seeds)\n  crfConPermImpVals <- data.frame(CondPermImp=sort(crfConPermImpVals/length(seeds), decreasing=TRUE))\n  #crfConPermImpValsMn <- sort(colMeans(crfConPermImpValsPerTree), decreasing=TRUE)  # mean of conditional variable importance values per tree (same as crfConPermImpVals)\n  crfConPermImpValsQtl <- data.frame(apply(crfConPermImpValsPerTree, 2, quantile, probs=c(0.25, 0.50, 0.75)))\n  \n  resultsOut <- list('OOB_RMSE'=crfOOBRMSE, 'OOB_MAE'=crfOOBMAE, 'Rsquared'=crfOOBErrR2, 'conditional_permimp'=crfConPermImpVals,                      'conditional_permimp_perTree'=crfConPermImpValsPerTree, 'conditional_permimp_qtl'=crfConPermImpValsQtl)\n  return(resultsOut)\n}\n\n```\n```\n\n<!-- rnb-source-end -->\n"} -->


<!-- rnb-source-begin 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 -->

```r
```r

multi_crfReg <- function(dataIn, iVars, dVar, seeds, ntree, mtry, permImpCondThres=0.95, minsplit=20, minbucket=7, nperm=1){
  # initialise variables
  crfOOBErrAll <- 0
  crfOOBRMSE <- 0
  crfOOBMAE <- 0
  crfOOBErrR2 <- 0
  crfMarPermImpVals <- 0
  crfConPermImpVals <- 0
  crfMarPermImpValsPerTree <- data.frame()
  crfConPermImpValsPerTree <- data.frame()
  
  for (iters in 1:length(seeds)){
    
    # formula for regression
    formVars <- reformulate(iVars, dVar)
    
    # set random seed
    set.seed(seeds[iters])
    # train crf model
    crfModel <- party::cforest(formVars, data=dataIn,
                               controls=party::cforest_unbiased(ntree=ntree,
                                                                mtry=mtry,
                                                                minsplit=minsplit,
                                                                minbucket=minbucket))
    
    # get OOB predictions
    crfModelOOB <- predict(crfModel, OOB=TRUE, type='response')
    
    # get OOB error
    crfModelOOBErr <- as.numeric(as.matrix(as.numeric(as.matrix(crfModelOOB))
                                            - as.numeric(as.matrix(crfModel@data@env$response[[names(crfModel@data@env$response)]]))))

    # OOB RMSE, error quartiles and Rsquared
    crfOOBRMSE <- crfOOBRMSE + sqrt(mean(crfModelOOBErr^2))
    crfOOBMAE <- crfOOBMAE + mean(abs(crfModelOOBErr))
    crfOOBErrR2 <- crfOOBErrR2 + cor(as.numeric(as.matrix(crfModelOOB)),
                                     as.numeric(as.matrix(crfModel@data@env$response[[names(crfModel@data@env$response)]])))^2

    # set random seed
    set.seed(seeds[iters])

    # set random seed
    set.seed(seeds[iters])
    # conditional variable permutation importance
    crfConPermImp <- permimp::permimp(crfModel, nperm=nperm, conditional=TRUE, threshold=permImpCondThres, progressBar=FALSE)
    
    crfConPermImpVals <- crfConPermImpVals + crfConPermImp$values
    crfConPermImpValsPerTree <- rbind(crfConPermImpValsPerTree, crfConPermImp$perTree)
  }
  
  # average metrics
  crfOOBErrAll <- crfOOBErrAll/length(seeds)
  crfOOBRMSE <- crfOOBRMSE/length(seeds)
  crfOOBMAE <- crfOOBMAE/length(seeds)
  crfOOBErrR2 <- crfOOBErrR2/length(seeds)
  crfConPermImpVals <- data.frame(CondPermImp=sort(crfConPermImpVals/length(seeds), decreasing=TRUE))
  #crfConPermImpValsMn <- sort(colMeans(crfConPermImpValsPerTree), decreasing=TRUE)  # mean of conditional variable importance values per tree (same as crfConPermImpVals)
  crfConPermImpValsQtl <- data.frame(apply(crfConPermImpValsPerTree, 2, quantile, probs=c(0.25, 0.50, 0.75)))
  
  resultsOut <- list('OOB_RMSE'=crfOOBRMSE, 'OOB_MAE'=crfOOBMAE, 'Rsquared'=crfOOBErrR2, 'conditional_permimp'=crfConPermImpVals,                      'conditional_permimp_perTree'=crfConPermImpValsPerTree, 'conditional_permimp_qtl'=crfConPermImpValsQtl)
  return(resultsOut)
}

```
```

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## Comparing rankings from two seeds 


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<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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 -->\n\n```r\n\ncrfReg <- function(dataIn, iVars, dVar, seeds, ntree, mtry, permImpCondThres=0.95, minsplit=20, minbucket=7, nperm=1){\n  # initialise variables\n  crfOOBErrAll <- 0\n  crfOOBRMSE <- 0\n  crfOOBMAE <- 0\n  crfOOBErrR2 <- 0\n  crfMarPermImpVals <- 0\n  crfConPermImpVals <- 0\n  crfMarPermImpValsPerTree <- data.frame()\n  crfConPermImpValsPerTree <- data.frame()\n\n  # formula for regression\n  formVars <- reformulate(iVars, dVar)\n  \n  for (iters in 1:length(seeds)){\n  \n    # set random seed\n    set.seed(seeds[iters])\n    # train crf model\n    crfModel <- party::cforest(formVars, data=dataIn,\n                               controls=party::cforest_unbiased(ntree=ntree,\n                                                                mtry=mtry,\n                                                                minsplit=minsplit,\n                                                                minbucket=minbucket))\n    \n    # conditional variable permutation importance\n    crfConPermImp <- permimp::permimp(crfModel, nperm=nperm, conditional=TRUE, threshold=permImpCondThres, progressBar=FALSE)\n    \n    crfConPermImpVals <- crfConPermImp$values\n    \n    if (iters == 1){\n      crfConPermImpVals1 <- data.frame(CondPermImp=sort(crfConPermImpVals, decreasing=TRUE))\n      crfConPermImpValsPerTree1 <- crfConPermImp$perTree\n      crfConPermImpValsQtl1 <- data.frame(apply(crfConPermImpValsPerTree1, 2, quantile, probs=c(0.25, 0.50, 0.75)))\n      \n      # get OOB predictions\n      crfModelOOB <- predict(crfModel, OOB=TRUE, type='response')\n      \n      # get OOB error\n      crfModelOOBErr <- as.numeric(as.matrix(as.numeric(as.matrix(crfModelOOB))\n                                              - as.numeric(as.matrix(crfModel@data@env$response[[names(crfModel@data@env$response)]]))))\n      \n      # OOB RMSE, error quartiles and Rsquared\n      crfOOBRMSE <- sqrt(mean(crfModelOOBErr^2))\n      crfOOBMAE <- crfOOBMAE + mean(abs(crfModelOOBErr))\n      crfOOBErrR2 <- cor(as.numeric(as.matrix(crfModelOOB)),\n                                    as.numeric(as.matrix(crfModel@data@env$response[[names(crfModel@data@env$response)]])))^2\n\n      }\n    \n    else{\n      crfConPermImpValsN <- data.frame(CondPermImp=sort(crfConPermImpVals, decreasing=TRUE))\n      \n      nVarImpChecks <- min(max(sum(crfConPermImpVals1 >= crfConPermImpVals1$CondPermImp[1]*0.1),\n                               sum(crfConPermImpValsN >= crfConPermImpValsN$CondPermImp[1]*0.1)), 4)  # number of variable importance values with a value at least 10% of the highest importance\n      if (sum(rownames(crfConPermImpVals1)[1:nVarImpChecks] != rownames(crfConPermImpValsN)[1:nVarImpChecks]) > 0){\n        warning(\\Permutation importance rank order within 10% of max differs between seeds: increase number of trees ('ntree') or number of permutations ('nperm')\n"} -->

crfReg <- function(dataIn, iVars, dVar, seeds, ntree, mtry, permImpCondThres=0.95, minsplit=20, minbucket=7, nperm=1){
  # initialise variables
  crfOOBErrAll <- 0
  crfOOBRMSE <- 0
  crfOOBMAE <- 0
  crfOOBErrR2 <- 0
  crfMarPermImpVals <- 0
  crfConPermImpVals <- 0
  crfMarPermImpValsPerTree <- data.frame()
  crfConPermImpValsPerTree <- data.frame()

  # formula for regression
  formVars <- reformulate(iVars, dVar)
  
  for (iters in 1:length(seeds)){
  
    # set random seed
    set.seed(seeds[iters])
    # train crf model
    crfModel <- party::cforest(formVars, data=dataIn,
                               controls=party::cforest_unbiased(ntree=ntree,
                                                                mtry=mtry,
                                                                minsplit=minsplit,
                                                                minbucket=minbucket))
    
    # conditional variable permutation importance
    crfConPermImp <- permimp::permimp(crfModel, nperm=nperm, conditional=TRUE, threshold=permImpCondThres, progressBar=FALSE)
    
    crfConPermImpVals <- crfConPermImp$values
    
    if (iters == 1){
      crfConPermImpVals1 <- data.frame(CondPermImp=sort(crfConPermImpVals, decreasing=TRUE))
      crfConPermImpValsPerTree1 <- crfConPermImp$perTree
      crfConPermImpValsQtl1 <- data.frame(apply(crfConPermImpValsPerTree1, 2, quantile, probs=c(0.25, 0.50, 0.75)))
      
      # get OOB predictions
      crfModelOOB <- predict(crfModel, OOB=TRUE, type='response')
      
      # get OOB error
      crfModelOOBErr <- as.numeric(as.matrix(as.numeric(as.matrix(crfModelOOB))
                                              - as.numeric(as.matrix(crfModel@data@env$response[[names(crfModel@data@env$response)]]))))
      
      # OOB RMSE, error quartiles and Rsquared
      crfOOBRMSE <- sqrt(mean(crfModelOOBErr^2))
      crfOOBMAE <- crfOOBMAE + mean(abs(crfModelOOBErr))
      crfOOBErrR2 <- cor(as.numeric(as.matrix(crfModelOOB)),
                                    as.numeric(as.matrix(crfModel@data@env$response[[names(crfModel@data@env$response)]])))^2

      }
    
    else{
      crfConPermImpValsN <- data.frame(CondPermImp=sort(crfConPermImpVals, decreasing=TRUE))
      
      nVarImpChecks <- min(max(sum(crfConPermImpVals1 >= crfConPermImpVals1$CondPermImp[1]*0.1),
                               sum(crfConPermImpValsN >= crfConPermImpValsN$CondPermImp[1]*0.1)), 4)  # number of variable importance values with a value at least 10% of the highest importance
      if (sum(rownames(crfConPermImpVals1)[1:nVarImpChecks] != rownames(crfConPermImpValsN)[1:nVarImpChecks]) > 0){
        warning(\Permutation importance rank order within 10% of max differs between seeds: increase number of trees ('ntree') or number of permutations ('nperm')
```r

crfReg <- function(dataIn, iVars, dVar, seeds, ntree, mtry, permImpCondThres=0.95, minsplit=20, minbucket=7, nperm=1){
  # initialise variables
  crfOOBErrAll <- 0
  crfOOBRMSE <- 0
  crfOOBMAE <- 0
  crfOOBErrR2 <- 0
  crfMarPermImpVals <- 0
  crfConPermImpVals <- 0
  crfMarPermImpValsPerTree <- data.frame()
  crfConPermImpValsPerTree <- data.frame()

  # formula for regression
  formVars <- reformulate(iVars, dVar)
  
  for (iters in 1:length(seeds)){
  
    # set random seed
    set.seed(seeds[iters])
    # train crf model
    crfModel <- party::cforest(formVars, data=dataIn,
                               controls=party::cforest_unbiased(ntree=ntree,
                                                                mtry=mtry,
                                                                minsplit=minsplit,
                                                                minbucket=minbucket))
    
    # conditional variable permutation importance
    crfConPermImp <- permimp::permimp(crfModel, nperm=nperm, conditional=TRUE, threshold=permImpCondThres, progressBar=FALSE)
    
    crfConPermImpVals <- crfConPermImp$values
    
    if (iters == 1){
      crfConPermImpVals1 <- data.frame(CondPermImp=sort(crfConPermImpVals, decreasing=TRUE))
      crfConPermImpValsPerTree1 <- crfConPermImp$perTree
      crfConPermImpValsQtl1 <- data.frame(apply(crfConPermImpValsPerTree1, 2, quantile, probs=c(0.25, 0.50, 0.75)))
      
      # get OOB predictions
      crfModelOOB <- predict(crfModel, OOB=TRUE, type='response')
      
      # get OOB error
      crfModelOOBErr <- as.numeric(as.matrix(as.numeric(as.matrix(crfModelOOB))
                                              - as.numeric(as.matrix(crfModel@data@env$response[[names(crfModel@data@env$response)]]))))
      
      # OOB RMSE, error quartiles and Rsquared
      crfOOBRMSE <- sqrt(mean(crfModelOOBErr^2))
      crfOOBMAE <- crfOOBMAE + mean(abs(crfModelOOBErr))
      crfOOBErrR2 <- cor(as.numeric(as.matrix(crfModelOOB)),
                                    as.numeric(as.matrix(crfModel@data@env$response[[names(crfModel@data@env$response)]])))^2

      }
    
    else{
      crfConPermImpValsN <- data.frame(CondPermImp=sort(crfConPermImpVals, decreasing=TRUE))
      
      nVarImpChecks <- min(max(sum(crfConPermImpVals1 >= crfConPermImpVals1$CondPermImp[1]*0.1),
                               sum(crfConPermImpValsN >= crfConPermImpValsN$CondPermImp[1]*0.1)), 4)  # number of variable importance values with a value at least 10% of the highest importance
      if (sum(rownames(crfConPermImpVals1)[1:nVarImpChecks] != rownames(crfConPermImpValsN)[1:nVarImpChecks]) > 0){
        warning(\Permutation importance rank order within 10% of max differs between seeds: increase number of trees ('ntree') or number of permutations ('nperm')

Hyperparameter tuning


<!-- rnb-source-begin {"data":"```r\nmtryTune <- function(dataIn, iVars, dVar, seeds, ntrees, minsplit=20, minbucket=7){\n\n  formVars <- reformulate(iVars, dVar)\n  \n  # set mtry values and corresponding iVars/mtry ratios\n  iVars_mtrys <- c(10.5, 5.25, 3.5, 2.75, 2.25, 1.75, 1.5, 1.25)\n  mtrys <- as.integer(length(iVars)/iVars_mtrys)\n  iVars_mtrys <- iVars_mtrys[mtrys >= 2]  # remove 0 or 1 values\n  mtrys <- mtrys[mtrys >= 2]  # remove 0 or 1 values\n  \n  # remove any duplicated values\n  iVars_mtrys <- iVars_mtrys[!(duplicated(mtrys) | duplicated(mtrys, fromLast = TRUE))]\n  mtrys <- mtrys[!(duplicated(mtrys) | duplicated(mtrys, fromLast = TRUE))]\n\n  # ensure mtrys is less than length(iVars)\n  iVars_mtrys <- iVars_mtrys[mtrys < length(iVars)]\n  mtrys <- mtrys[mtrys < length(iVars)]\n\n  resRMSEMap = matrix(data=0, nrow=length(mtrys), ncol=length(ntrees))\n  resRsquaredMap = matrix(data=0, nrow=length(mtrys), ncol=length(ntrees))\n  resMAEMap = matrix(data=0, nrow=length(mtrys), ncol=length(ntrees))\n  \n  \n  for (ii in seq(1, length(ntrees))){\n    \n    tuneMod.results <- data.frame(RMSE=numeric(length(mtrys)),\n                                Rsquared=numeric(length(mtrys)),\n                                MAE=numeric(length(mtrys)))\n    \n    for (seed in seeds){\n      set.seed(seed)\n      ntree = ntrees[ii]\n      tuneMod <- caret::train(formVars,\n                              data=dataIn,\n                              method='cforest',\n                              controls=party::cforest_unbiased(ntree=ntree,\n                                                               minsplit=minsplit,\n                                                               minbucket=minbucket),\n                              tuneGrid=data.frame(.mtry=mtrys),\n                              trControl = trainControl(method = \"oob\"))\n      \n      \n      \n      # accumulate results\n      resRMSEMap[, ii] <- resRMSEMap[, ii] + tuneMod$results$RMSE\n      resRsquaredMap[, ii] <- resRsquaredMap[, ii] + tuneMod$results$Rsquared\n      resMAEMap[, ii] <- resMAEMap[, ii] + tuneMod$results$MAE\n      \n      tuneMod.results <- tuneMod.results + tuneMod$results[, which(names(tuneMod$results) != 'mtry')]\n    }\n\n    # average results\n    tuneMod.results <- tuneMod.results/length(seeds)\n    tuneMod.results <- cbind(tuneMod.results, data.frame(mtry=mtrys), data.frame(iVars_mtry=iVars_mtrys))\n\n    print(tuneMod.results)\n\n  }\n  \n  # average results\n  resRMSEMap <- resRMSEMap/length(seeds)\n  resRsquaredMap <- resRsquaredMap/length(seeds)\n  resMAEMap <- resMAEMap/length(seeds)\n  \n  \n  # convert to data frames with mtry as row names and ntree as column names, and convert to long format using tidyverse\n  resdfRMSEMap <- as.data.frame(resRMSEMap)\n  rownames(resdfRMSEMap) <- mtrys\n  colnames(resdfRMSEMap) <- ntrees\n  resdfRsquaredMap <- as.data.frame(resRsquaredMap)\n  rownames(resdfRsquaredMap) <- mtrys\n  colnames(resdfRsquaredMap) <- ntrees\n  resdfMAEMap <- as.data.frame(resMAEMap)\n  rownames(resdfMAEMap) <- mtrys\n  colnames(resdfMAEMap) <- ntrees\n  \n  \n  # convert dataframes to long format using tidyverse\n  resdfRMSEMap <- resdfRMSEMap |>\n                      rownames_to_column('mtry') |>\n                          gather(key='ntree', value='RMSE', -mtry)\n  \n  resdfRsquaredMap <- resdfRsquaredMap |>\n                          rownames_to_column('mtry') |>\n                              gather(key='ntree', value='Rsquared', -mtry)\n  \n  resdfMAEMap <- resdfMAEMap |>\n                    rownames_to_column('mtry') |>\n                        gather(key='ntree', value='MAE', -mtry)\n  \n  # ensure ntree and mtry columns are ordered factors\n  resdfRMSEMap$ntree <- factor(resdfRMSEMap$ntree, levels=as.character(ntrees))\n  resdfRMSEMap$mtry <- factor(resdfRMSEMap$mtry, levels=as.character(mtrys))\n  \n  resdfRsquaredMap$ntree <- factor(resdfRsquaredMap$ntree, levels=as.character(ntrees))\n  resdfRsquaredMap$mtry <- factor(resdfRsquaredMap$mtry, levels=as.character(mtrys))\n  \n  resdfMAEMap$ntree <- factor(resdfMAEMap$ntree, levels=as.character(ntrees))\n  resdfMAEMap$mtry <- factor(resdfMAEMap$mtry, levels=as.character(mtrys))\n  \n  # plot heatmaps using ggplot, with extreme (min or max) value plotted as overlaid point using annotate and colourbar scale reversed\n  pHeatmapRMSE <- ggplot(resdfRMSEMap) +\n                    geom_tile(aes(x=ntree, y=mtry, fill=RMSE)) +\n                        scale_fill_viridis(option=\"viridis\", direction=-1) +\n                          geom_point(data=resdfRMSEMap[which(resdfRMSEMap$RMSE\n                                                             == min(resdfRMSEMap$RMSE),\n                                                             arr.ind = TRUE),],\n                                     aes(x=ntree, y=mtry), colour=\"red\", size=2) +\n                            guides(colour = guide_colourbar(reverse=TRUE)) +\n                              labs(x=\"ntree\", y=\"mtry\", fill=\"RMSE\") +\n                                theme(text = element_text(family = \"serif\"))\n  \n  pHeatmapRsquared <- ggplot(resdfRsquaredMap) +\n                        geom_tile(aes(x=ntree, y=mtry, fill=Rsquared)) +\n                            scale_fill_viridis(option=\"viridis\", direction=1) +\n                              geom_point(data=resdfRsquaredMap[which(resdfRsquaredMap$Rsquared\n                                                                     == max(resdfRsquaredMap$Rsquared),\n                                                                     arr.ind = TRUE),],\n                                         aes(x=ntree, y=mtry), colour=\"red\", size=2) +\n                                guides(colour = guide_colourbar(reverse=TRUE)) +\n                                  labs(x=\"ntree\", y=\"mtry\", fill=\"Rsquared\") +\n                                    theme(text = element_text(family = \"serif\"))\n  \n  pHeatmapMAE <- ggplot(resdfMAEMap) +\n                    geom_tile(aes(x=ntree, y=mtry, fill=MAE)) +\n                        scale_fill_viridis(option=\"viridis\", direction=-1) +\n                          geom_point(data=resdfMAEMap[which(resdfMAEMap$MAE\n                                                            == min(resdfMAEMap$MAE),\n                                                            arr.ind = TRUE),],\n                                     aes(x=ntree, y=mtry), colour=\"red\", size=2) +\n                            guides(colour = guide_colourbar(reverse=TRUE)) +\n                              labs(x=\"ntree\", y=\"mtry\", fill=\"MAE\") +\n                                theme(text = element_text(family = \"serif\"))\n  \n  p <-  cowplot::plot_grid(pHeatmapRMSE,\n                           pHeatmapRsquared,\n                           pHeatmapMAE,\n                           ncol=3, nrow=1)\n  \n  return(p)\n  \n}  # end of function\n\n```"} -->

```r
mtryTune <- function(dataIn, iVars, dVar, seeds, ntrees, minsplit=20, minbucket=7){

  formVars <- reformulate(iVars, dVar)
  
  # set mtry values and corresponding iVars/mtry ratios
  iVars_mtrys <- c(10.5, 5.25, 3.5, 2.75, 2.25, 1.75, 1.5, 1.25)
  mtrys <- as.integer(length(iVars)/iVars_mtrys)
  iVars_mtrys <- iVars_mtrys[mtrys >= 2]  # remove 0 or 1 values
  mtrys <- mtrys[mtrys >= 2]  # remove 0 or 1 values
  
  # remove any duplicated values
  iVars_mtrys <- iVars_mtrys[!(duplicated(mtrys) | duplicated(mtrys, fromLast = TRUE))]
  mtrys <- mtrys[!(duplicated(mtrys) | duplicated(mtrys, fromLast = TRUE))]

  # ensure mtrys is less than length(iVars)
  iVars_mtrys <- iVars_mtrys[mtrys < length(iVars)]
  mtrys <- mtrys[mtrys < length(iVars)]

  resRMSEMap = matrix(data=0, nrow=length(mtrys), ncol=length(ntrees))
  resRsquaredMap = matrix(data=0, nrow=length(mtrys), ncol=length(ntrees))
  resMAEMap = matrix(data=0, nrow=length(mtrys), ncol=length(ntrees))
  
  
  for (ii in seq(1, length(ntrees))){
    
    tuneMod.results <- data.frame(RMSE=numeric(length(mtrys)),
                                Rsquared=numeric(length(mtrys)),
                                MAE=numeric(length(mtrys)))
    
    for (seed in seeds){
      set.seed(seed)
      ntree = ntrees[ii]
      tuneMod <- caret::train(formVars,
                              data=dataIn,
                              method='cforest',
                              controls=party::cforest_unbiased(ntree=ntree,
                                                               minsplit=minsplit,
                                                               minbucket=minbucket),
                              tuneGrid=data.frame(.mtry=mtrys),
                              trControl = trainControl(method = \oob\))
      
      
      
      # accumulate results
      resRMSEMap[, ii] <- resRMSEMap[, ii] + tuneMod$results$RMSE
      resRsquaredMap[, ii] <- resRsquaredMap[, ii] + tuneMod$results$Rsquared
      resMAEMap[, ii] <- resMAEMap[, ii] + tuneMod$results$MAE
      
      tuneMod.results <- tuneMod.results + tuneMod$results[, which(names(tuneMod$results) != 'mtry')]
    }

    # average results
    tuneMod.results <- tuneMod.results/length(seeds)
    tuneMod.results <- cbind(tuneMod.results, data.frame(mtry=mtrys), data.frame(iVars_mtry=iVars_mtrys))

    print(tuneMod.results)

  }
  
  # average results
  resRMSEMap <- resRMSEMap/length(seeds)
  resRsquaredMap <- resRsquaredMap/length(seeds)
  resMAEMap <- resMAEMap/length(seeds)
  
  
  # convert to data frames with mtry as row names and ntree as column names, and convert to long format using tidyverse
  resdfRMSEMap <- as.data.frame(resRMSEMap)
  rownames(resdfRMSEMap) <- mtrys
  colnames(resdfRMSEMap) <- ntrees
  resdfRsquaredMap <- as.data.frame(resRsquaredMap)
  rownames(resdfRsquaredMap) <- mtrys
  colnames(resdfRsquaredMap) <- ntrees
  resdfMAEMap <- as.data.frame(resMAEMap)
  rownames(resdfMAEMap) <- mtrys
  colnames(resdfMAEMap) <- ntrees
  
  
  # convert dataframes to long format using tidyverse
  resdfRMSEMap <- resdfRMSEMap |>
                      rownames_to_column('mtry') |>
                          gather(key='ntree', value='RMSE', -mtry)
  
  resdfRsquaredMap <- resdfRsquaredMap |>
                          rownames_to_column('mtry') |>
                              gather(key='ntree', value='Rsquared', -mtry)
  
  resdfMAEMap <- resdfMAEMap |>
                    rownames_to_column('mtry') |>
                        gather(key='ntree', value='MAE', -mtry)
  
  # ensure ntree and mtry columns are ordered factors
  resdfRMSEMap$ntree <- factor(resdfRMSEMap$ntree, levels=as.character(ntrees))
  resdfRMSEMap$mtry <- factor(resdfRMSEMap$mtry, levels=as.character(mtrys))
  
  resdfRsquaredMap$ntree <- factor(resdfRsquaredMap$ntree, levels=as.character(ntrees))
  resdfRsquaredMap$mtry <- factor(resdfRsquaredMap$mtry, levels=as.character(mtrys))
  
  resdfMAEMap$ntree <- factor(resdfMAEMap$ntree, levels=as.character(ntrees))
  resdfMAEMap$mtry <- factor(resdfMAEMap$mtry, levels=as.character(mtrys))
  
  # plot heatmaps using ggplot, with extreme (min or max) value plotted as overlaid point using annotate and colourbar scale reversed
  pHeatmapRMSE <- ggplot(resdfRMSEMap) +
                    geom_tile(aes(x=ntree, y=mtry, fill=RMSE)) +
                        scale_fill_viridis(option=\viridis\, direction=-1) +
                          geom_point(data=resdfRMSEMap[which(resdfRMSEMap$RMSE
                                                             == min(resdfRMSEMap$RMSE),
                                                             arr.ind = TRUE),],
                                     aes(x=ntree, y=mtry), colour=\red\, size=2) +
                            guides(colour = guide_colourbar(reverse=TRUE)) +
                              labs(x=\ntree\, y=\mtry\, fill=\RMSE\) +
                                theme(text = element_text(family = \serif\))
  
  pHeatmapRsquared <- ggplot(resdfRsquaredMap) +
                        geom_tile(aes(x=ntree, y=mtry, fill=Rsquared)) +
                            scale_fill_viridis(option=\viridis\, direction=1) +
                              geom_point(data=resdfRsquaredMap[which(resdfRsquaredMap$Rsquared
                                                                     == max(resdfRsquaredMap$Rsquared),
                                                                     arr.ind = TRUE),],
                                         aes(x=ntree, y=mtry), colour=\red\, size=2) +
                                guides(colour = guide_colourbar(reverse=TRUE)) +
                                  labs(x=\ntree\, y=\mtry\, fill=\Rsquared\) +
                                    theme(text = element_text(family = \serif\))
  
  pHeatmapMAE <- ggplot(resdfMAEMap) +
                    geom_tile(aes(x=ntree, y=mtry, fill=MAE)) +
                        scale_fill_viridis(option=\viridis\, direction=-1) +
                          geom_point(data=resdfMAEMap[which(resdfMAEMap$MAE
                                                            == min(resdfMAEMap$MAE),
                                                            arr.ind = TRUE),],
                                     aes(x=ntree, y=mtry), colour=\red\, size=2) +
                            guides(colour = guide_colourbar(reverse=TRUE)) +
                              labs(x=\ntree\, y=\mtry\, fill=\MAE\) +
                                theme(text = element_text(family = \serif\))
  
  p <-  cowplot::plot_grid(pHeatmapRMSE,
                           pHeatmapRsquared,
                           pHeatmapMAE,
                           ncol=3, nrow=1)
  
  return(p)
  
}  # end of function

```

<!-- rnb-source-end -->
```r
mtryTune <- function(dataIn, iVars, dVar, seeds, ntrees, minsplit=20, minbucket=7){

  formVars <- reformulate(iVars, dVar)
  
  # set mtry values and corresponding iVars/mtry ratios
  iVars_mtrys <- c(10.5, 5.25, 3.5, 2.75, 2.25, 1.75, 1.5, 1.25)
  mtrys <- as.integer(length(iVars)/iVars_mtrys)
  iVars_mtrys <- iVars_mtrys[mtrys >= 2]  # remove 0 or 1 values
  mtrys <- mtrys[mtrys >= 2]  # remove 0 or 1 values
  
  # remove any duplicated values
  iVars_mtrys <- iVars_mtrys[!(duplicated(mtrys) | duplicated(mtrys, fromLast = TRUE))]
  mtrys <- mtrys[!(duplicated(mtrys) | duplicated(mtrys, fromLast = TRUE))]

  # ensure mtrys is less than length(iVars)
  iVars_mtrys <- iVars_mtrys[mtrys < length(iVars)]
  mtrys <- mtrys[mtrys < length(iVars)]

  resRMSEMap = matrix(data=0, nrow=length(mtrys), ncol=length(ntrees))
  resRsquaredMap = matrix(data=0, nrow=length(mtrys), ncol=length(ntrees))
  resMAEMap = matrix(data=0, nrow=length(mtrys), ncol=length(ntrees))
  
  
  for (ii in seq(1, length(ntrees))){
    
    tuneMod.results <- data.frame(RMSE=numeric(length(mtrys)),
                                Rsquared=numeric(length(mtrys)),
                                MAE=numeric(length(mtrys)))
    
    for (seed in seeds){
      set.seed(seed)
      ntree = ntrees[ii]
      tuneMod <- caret::train(formVars,
                              data=dataIn,
                              method='cforest',
                              controls=party::cforest_unbiased(ntree=ntree,
                                                               minsplit=minsplit,
                                                               minbucket=minbucket),
                              tuneGrid=data.frame(.mtry=mtrys),
                              trControl = trainControl(method = \oob\))
      
      
      
      # accumulate results
      resRMSEMap[, ii] <- resRMSEMap[, ii] + tuneMod$results$RMSE
      resRsquaredMap[, ii] <- resRsquaredMap[, ii] + tuneMod$results$Rsquared
      resMAEMap[, ii] <- resMAEMap[, ii] + tuneMod$results$MAE
      
      tuneMod.results <- tuneMod.results + tuneMod$results[, which(names(tuneMod$results) != 'mtry')]
    }

    # average results
    tuneMod.results <- tuneMod.results/length(seeds)
    tuneMod.results <- cbind(tuneMod.results, data.frame(mtry=mtrys), data.frame(iVars_mtry=iVars_mtrys))

    print(tuneMod.results)

  }
  
  # average results
  resRMSEMap <- resRMSEMap/length(seeds)
  resRsquaredMap <- resRsquaredMap/length(seeds)
  resMAEMap <- resMAEMap/length(seeds)
  
  
  # convert to data frames with mtry as row names and ntree as column names, and convert to long format using tidyverse
  resdfRMSEMap <- as.data.frame(resRMSEMap)
  rownames(resdfRMSEMap) <- mtrys
  colnames(resdfRMSEMap) <- ntrees
  resdfRsquaredMap <- as.data.frame(resRsquaredMap)
  rownames(resdfRsquaredMap) <- mtrys
  colnames(resdfRsquaredMap) <- ntrees
  resdfMAEMap <- as.data.frame(resMAEMap)
  rownames(resdfMAEMap) <- mtrys
  colnames(resdfMAEMap) <- ntrees
  
  
  # convert dataframes to long format using tidyverse
  resdfRMSEMap <- resdfRMSEMap |>
                      rownames_to_column('mtry') |>
                          gather(key='ntree', value='RMSE', -mtry)
  
  resdfRsquaredMap <- resdfRsquaredMap |>
                          rownames_to_column('mtry') |>
                              gather(key='ntree', value='Rsquared', -mtry)
  
  resdfMAEMap <- resdfMAEMap |>
                    rownames_to_column('mtry') |>
                        gather(key='ntree', value='MAE', -mtry)
  
  # ensure ntree and mtry columns are ordered factors
  resdfRMSEMap$ntree <- factor(resdfRMSEMap$ntree, levels=as.character(ntrees))
  resdfRMSEMap$mtry <- factor(resdfRMSEMap$mtry, levels=as.character(mtrys))
  
  resdfRsquaredMap$ntree <- factor(resdfRsquaredMap$ntree, levels=as.character(ntrees))
  resdfRsquaredMap$mtry <- factor(resdfRsquaredMap$mtry, levels=as.character(mtrys))
  
  resdfMAEMap$ntree <- factor(resdfMAEMap$ntree, levels=as.character(ntrees))
  resdfMAEMap$mtry <- factor(resdfMAEMap$mtry, levels=as.character(mtrys))
  
  # plot heatmaps using ggplot, with extreme (min or max) value plotted as overlaid point using annotate and colourbar scale reversed
  pHeatmapRMSE <- ggplot(resdfRMSEMap) +
                    geom_tile(aes(x=ntree, y=mtry, fill=RMSE)) +
                        scale_fill_viridis(option=\viridis\, direction=-1) +
                          geom_point(data=resdfRMSEMap[which(resdfRMSEMap$RMSE
                                                             == min(resdfRMSEMap$RMSE),
                                                             arr.ind = TRUE),],
                                     aes(x=ntree, y=mtry), colour=\red\, size=2) +
                            guides(colour = guide_colourbar(reverse=TRUE)) +
                              labs(x=\ntree\, y=\mtry\, fill=\RMSE\) +
                                theme(text = element_text(family = \serif\))
  
  pHeatmapRsquared <- ggplot(resdfRsquaredMap) +
                        geom_tile(aes(x=ntree, y=mtry, fill=Rsquared)) +
                            scale_fill_viridis(option=\viridis\, direction=1) +
                              geom_point(data=resdfRsquaredMap[which(resdfRsquaredMap$Rsquared
                                                                     == max(resdfRsquaredMap$Rsquared),
                                                                     arr.ind = TRUE),],
                                         aes(x=ntree, y=mtry), colour=\red\, size=2) +
                                guides(colour = guide_colourbar(reverse=TRUE)) +
                                  labs(x=\ntree\, y=\mtry\, fill=\Rsquared\) +
                                    theme(text = element_text(family = \serif\))
  
  pHeatmapMAE <- ggplot(resdfMAEMap) +
                    geom_tile(aes(x=ntree, y=mtry, fill=MAE)) +
                        scale_fill_viridis(option=\viridis\, direction=-1) +
                          geom_point(data=resdfMAEMap[which(resdfMAEMap$MAE
                                                            == min(resdfMAEMap$MAE),
                                                            arr.ind = TRUE),],
                                     aes(x=ntree, y=mtry), colour=\red\, size=2) +
                            guides(colour = guide_colourbar(reverse=TRUE)) +
                              labs(x=\ntree\, y=\mtry\, fill=\MAE\) +
                                theme(text = element_text(family = \serif\))
  
  p <-  cowplot::plot_grid(pHeatmapRMSE,
                           pHeatmapRsquared,
                           pHeatmapMAE,
                           ncol=3, nrow=1)
  
  return(p)
  
}  # end of function

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->



# Parts A & B analysis


## Set global parameters


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->

permImpCondThres <- 0.95
minsplit <- 20
minbucket <- 7
ntrees <- c(251, 501, 1001, 1501, 2501, 4001, 5501)

eventVar <- \UASEvents\
ambVar <- \AmbientLAeq\



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5wZXJtSW1wQ29uZFRocmVzIDwtIDAuOTVcbm1pbnNwbGl0IDwtIDIwXG5taW5idWNrZXQgPC0gN1xubnRyZWVzIDwtIGMoMjUxLCA1MDEsIDEwMDEsIDE1MDEsIDI1MDEsIDQwMDEsIDU1MDEpXG5cbmV2ZW50VmFyIDwtIFxcVUFTRXZlbnRzXFxcbmFtYlZhciA8LSBcXEFtYmllbnRMQWVxXFxcblxuYGBgXG5gYGAifQ== -->

```r
```r

permImpCondThres <- 0.95
minsplit <- 20
minbucket <- 7
ntrees <- c(251, 501, 1001, 1501, 2501, 4001, 5501)

eventVar <- \UASEvents\
ambVar <- \AmbientLAeq\

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


## Mean change in annoyance

### Initialise results output variables


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->
resdAnnoyMnFitAB <- data.frame(RMSE = numeric(),
                             MAE = numeric(),
                             Rsquared = numeric())
resdAnnoyMnPermImpAB <- list()



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucmVzZEFubm95TW5GaXRBQiA8LSBkYXRhLmZyYW1lKFJNU0UgPSBudW1lcmljKCksXG4gICAgICAgICAgICAgICAgICAgICAgICAgICAgIE1BRSA9IG51bWVyaWMoKSxcbiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgUnNxdWFyZWQgPSBudW1lcmljKCkpXG5yZXNkQW5ub3lNblBlcm1JbXBBQiA8LSBsaXN0KClcblxuYGBgXG5gYGAifQ== -->

```r
```r
resdAnnoyMnFitAB <- data.frame(RMSE = numeric(),
                             MAE = numeric(),
                             Rsquared = numeric())
resdAnnoyMnPermImpAB <- list()

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

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<!-- rnb-text-begin -->



### All variables (absolute and difference)

#### Remove ambient only stimuli - OMITTED

Here, the 'ambient only' stimuli are removed, as the analysis cannot handle missing values for dB metrics.


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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# stimDataNum <- stimDataNum[complete.cases(stimDataNum),]



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dUl5QnpkR2x0UkdGMFlVNTFiU0E4TFNCemRHbHRSR0YwWVU1MWJWdGpiMjF3YkdWMFpTNWpZWE5sY3loemRHbHRSR0YwWVU1MWJTa3NYVnh1WEc1Z1lHQmNibUJnWUNKOSAtLT5cblxuYGBgclxuYGBgclxuIyBzdGltRGF0YU51bSA8LSBzdGltRGF0YU51bVtjb21wbGV0ZS5jYXNlcyhzdGltRGF0YU51bSksXVxuXG5gYGBcbmBgYFxuXG48IS0tIHJuYi1zb3VyY2UtZW5kIC0tPlxuIn0= -->


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```r
```r
# stimDataNum <- stimDataNum[complete.cases(stimDataNum),]

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


#### Set variables


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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iVars <- names(stimDataNum)[which(names(stimDataNum) == 'UASEvents'):which(names(stimDataNum) == 'UASImpulsSHM05ExMaxLR')]
iVars <- c(iVars,
           names(stimDataNum)[which(colnames(stimDataNum)==\LAeqLAF90diff\):
                               which(colnames(stimDataNum)==\dImpulsSHM05ExMaxLR\)], \SNRlevel\)
dVar <- \dAnnoyMean\

seeds <- c(14569, 98651, 54654498, 454948, 41321)



<!-- rnb-output-end -->

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```r
```r

iVars <- names(stimDataNum)[which(names(stimDataNum) == 'UASEvents'):which(names(stimDataNum) == 'UASImpulsSHM05ExMaxLR')]
iVars <- c(iVars,
           names(stimDataNum)[which(colnames(stimDataNum)==\LAeqLAF90diff\):
                               which(colnames(stimDataNum)==\dImpulsSHM05ExMaxLR\)], \SNRlevel\)
dVar <- \dAnnoyMean\

seeds <- c(14569, 98651, 54654498, 454948, 41321)

```
```

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<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


#### Hyperparameter tuning


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
              ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)



<!-- rnb-output-end -->

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```r
```r

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
              ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
```
```

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<!-- rnb-output-end -->

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\" 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" />

<!-- rnb-plot-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->

if (saveplots){
  ggsave(filename=\PtsABdAnnoyMnAllVarsHyperTune.svg\, width=12, height=4, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdAnnoyMnAllVarsHyperTune.svg\)

  ggsave(filename=\PtsABdAnnoyMnAllVarsHyperTune.pdf\, width=12, height=4, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdAnnoyMnAllVarsHyperTune.pdf\)
}



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


<!-- rnb-source-begin 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 -->

```r
```r

if (saveplots){
  ggsave(filename=\PtsABdAnnoyMnAllVarsHyperTune.svg\, width=12, height=4, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdAnnoyMnAllVarsHyperTune.svg\)

  ggsave(filename=\PtsABdAnnoyMnAllVarsHyperTune.pdf\, width=12, height=4, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdAnnoyMnAllVarsHyperTune.pdf\)
}

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Selected hyperparameters


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVYRzV1ZEhKbFpTQThMU0F4TlRBeFhHNXRkSEo1SUR3dElHRnpMbWx1ZEdWblpYSW9iR1Z1WjNSb0tHbFdZWEp6S1M4eUxqSTFLVnh1WEc1Z1lHQWlmUT09IC0tPlxuXG5gYGByXG5cbm50cmVlIDwtIDE1MDFcbm10cnkgPC0gYXMuaW50ZWdlcihsZW5ndGgoaVZhcnMpLzIuMjUpXG5cbmBgYFxuXG48IS0tIHJuYi1zb3VyY2UtZW5kIC0tPlxuIn0= -->

ntree <- 1501
mtry <- as.integer(length(iVars)/2.25)



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dVhHNXVkSEpsWlNBOExTQXhOVEF4WEc1dGRISjVJRHd0SUdGekxtbHVkR1ZuWlhJb2JHVnVaM1JvS0dsV1lYSnpLUzh5TGpJMUtWeHVYRzVnWUdCY2JtQmdZQ0o5IC0tPlxuXG5gYGByXG5gYGByXG5cbm50cmVlIDwtIDE1MDFcbm10cnkgPC0gYXMuaW50ZWdlcihsZW5ndGgoaVZhcnMpLzIuMjUpXG5cbmBgYFxuYGBgXG5cbjwhLS0gcm5iLXNvdXJjZS1lbmQgLS0+XG4ifQ== -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5udHJlZSA8LSAxNTAxXG5tdHJ5IDwtIGFzLmludGVnZXIobGVuZ3RoKGlWYXJzKS8yLjI1KVxuXG5gYGBcbmBgYCJ9 -->

```r
```r

ntree <- 1501
mtry <- as.integer(length(iVars)/2.25)

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


#### Run model

Train preliminary model


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->

nperm <- 5

resultsOutAbsDiffs <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutAbsDiffs$OOB_RMSE



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dVhHNXVjR1Z5YlNBOExTQTFYRzVjYm5KbGMzVnNkSE5QZFhSQlluTkVhV1ptY3lBOExTQmpjbVpTWldjb1pHRjBZVWx1UFhOMGFXMUVZWFJoVG5WdExDQnBWbUZ5Y3oxcFZtRnljeXdnWkZaaGNqMWtWbUZ5TENCelpXVmtjejF6WldWa2Mxc3hPakpkTENCdWRISmxaVDF1ZEhKbFpTd2diWFJ5ZVQxdGRISjVMQ0J3WlhKdFNXMXdRMjl1WkZSb2NtVnpQWEJsY20xSmJYQkRiMjVrVkdoeVpYTXNJRzV3WlhKdFBXNXdaWEp0TENCdGFXNXpjR3hwZEQxdGFXNXpjR3hwZEN3Z2JXbHVZblZqYTJWMFBXMXBibUoxWTJ0bGRDbGNibHh1SXlCd2NtbHVkQ0J0YjJSbGJDQndjbVZrYVdOMGFXOXVJSEpsYzNWc2RITmNibkpsYzNWc2RITlBkWFJCWW5ORWFXWm1jeVJQVDBKZlVrMVRSVnh1WUdCZ1hHNWdZR0FpZlE9PSAtLT5cblxuYGBgclxuYGBgclxuXG5ucGVybSA8LSA1XG5cbnJlc3VsdHNPdXRBYnNEaWZmcyA8LSBjcmZSZWcoZGF0YUluPXN0aW1EYXRhTnVtLCBpVmFycz1pVmFycywgZFZhcj1kVmFyLCBzZWVkcz1zZWVkc1sxOjJdLCBudHJlZT1udHJlZSwgbXRyeT1tdHJ5LCBwZXJtSW1wQ29uZFRocmVzPXBlcm1JbXBDb25kVGhyZXMsIG5wZXJtPW5wZXJtLCBtaW5zcGxpdD1taW5zcGxpdCwgbWluYnVja2V0PW1pbmJ1Y2tldClcblxuIyBwcmludCBtb2RlbCBwcmVkaWN0aW9uIHJlc3VsdHNcbnJlc3VsdHNPdXRBYnNEaWZmcyRPT0JfUk1TRVxuYGBgXG5gYGBcblxuPCEtLSBybmItc291cmNlLWVuZCAtLT5cbiJ9 -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5ucGVybSA8LSA1XG5cbnJlc3VsdHNPdXRBYnNEaWZmcyA8LSBjcmZSZWcoZGF0YUluPXN0aW1EYXRhTnVtLCBpVmFycz1pVmFycywgZFZhcj1kVmFyLCBzZWVkcz1zZWVkc1sxOjJdLCBudHJlZT1udHJlZSwgbXRyeT1tdHJ5LCBwZXJtSW1wQ29uZFRocmVzPXBlcm1JbXBDb25kVGhyZXMsIG5wZXJtPW5wZXJtLCBtaW5zcGxpdD1taW5zcGxpdCwgbWluYnVja2V0PW1pbmJ1Y2tldClcblxuIyBwcmludCBtb2RlbCBwcmVkaWN0aW9uIHJlc3VsdHNcbnJlc3VsdHNPdXRBYnNEaWZmcyRPT0JfUk1TRVxuYGBgXG5gYGAifQ== -->

```r
```r

nperm <- 5

resultsOutAbsDiffs <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutAbsDiffs$OOB_RMSE
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDAuNTI1NjQzOFxuXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVjbVZ6ZFd4MGMwOTFkRUZpYzBScFptWnpKRTlQUWw5TlFVVmNibUJnWUNKOSAtLT5cblxuYGBgclxucmVzdWx0c091dEFic0RpZmZzJE9PQl9NQUVcbmBgYFxuXG48IS0tIHJuYi1zb3VyY2UtZW5kIC0tPlxuIn0= -->

[1] 0.5256438

resultsOutAbsDiffs$OOB_MAE



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dWNtVnpkV3gwYzA5MWRFRmljMFJwWm1aekpFOVBRbDlOUVVWY2JtQmdZRnh1WUdCZ0luMD0gLS0+XG5cbmBgYHJcbmBgYHJcbnJlc3VsdHNPdXRBYnNEaWZmcyRPT0JfTUFFXG5gYGBcbmBgYFxuXG48IS0tIHJuYi1zb3VyY2UtZW5kIC0tPlxuIn0= -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucmVzdWx0c091dEFic0RpZmZzJE9PQl9NQUVcbmBgYFxuYGBgIn0= -->

```r
```r
resultsOutAbsDiffs$OOB_MAE
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDAuNDA2NjM0NVxuXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVjbVZ6ZFd4MGMwOTFkRUZpYzBScFptWnpKRkp6Y1hWaGNtVmtYRzVnWUdBaWZRPT0gLS0+XG5cbmBgYHJcbnJlc3VsdHNPdXRBYnNEaWZmcyRSc3F1YXJlZFxuYGBgXG5cbjwhLS0gcm5iLXNvdXJjZS1lbmQgLS0+XG4ifQ== -->

[1] 0.4066345

resultsOutAbsDiffs$Rsquared



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dWNtVnpkV3gwYzA5MWRFRmljMFJwWm1aekpGSnpjWFZoY21Wa1hHNWdZR0JjYm1CZ1lDSjkgLS0+XG5cbmBgYHJcbmBgYHJcbnJlc3VsdHNPdXRBYnNEaWZmcyRSc3F1YXJlZFxuYGBgXG5gYGBcblxuPCEtLSBybmItc291cmNlLWVuZCAtLT5cbiJ9 -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucmVzdWx0c091dEFic0RpZmZzJFJzcXVhcmVkXG5gYGBcbmBgYCJ9 -->

```r
```r
resultsOutAbsDiffs$Rsquared
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDAuODkxODkwOVxuIn0= -->

```
[1] 0.8918909
```



<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Train multiple seeds model


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->

resultsOutAbsDiffs <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutAbsDiffs$OOB_RMSE



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5yZXN1bHRzT3V0QWJzRGlmZnMgPC0gbXVsdGlfY3JmUmVnKGRhdGFJbj1zdGltRGF0YU51bSwgaVZhcnM9aVZhcnMsIGRWYXI9ZFZhciwgc2VlZHM9c2VlZHMsIG50cmVlPW50cmVlLCBtdHJ5PW10cnksIHBlcm1JbXBDb25kVGhyZXM9cGVybUltcENvbmRUaHJlcywgbnBlcm09bnBlcm0sIG1pbnNwbGl0PW1pbnNwbGl0LCBtaW5idWNrZXQ9bWluYnVja2V0KVxuXG4jIHByaW50IG1vZGVsIHByZWRpY3Rpb24gcmVzdWx0c1xucmVzdWx0c091dEFic0RpZmZzJE9PQl9STVNFXG5gYGBcbmBgYCJ9 -->

```r
```r

resultsOutAbsDiffs <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutAbsDiffs$OOB_RMSE
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDAuNTI1MTc5NFxuXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVjbVZ6ZFd4MGMwOTFkRUZpYzBScFptWnpKRTlQUWw5TlFVVmNibUJnWUNKOSAtLT5cblxuYGBgclxucmVzdWx0c091dEFic0RpZmZzJE9PQl9NQUVcbmBgYFxuXG48IS0tIHJuYi1zb3VyY2UtZW5kIC0tPlxuIn0= -->

[1] 0.5251794

resultsOutAbsDiffs$OOB_MAE



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dWNtVnpkV3gwYzA5MWRFRmljMFJwWm1aekpFOVBRbDlOUVVWY2JtQmdZRnh1WUdCZ0luMD0gLS0+XG5cbmBgYHJcbmBgYHJcbnJlc3VsdHNPdXRBYnNEaWZmcyRPT0JfTUFFXG5gYGBcbmBgYFxuXG48IS0tIHJuYi1zb3VyY2UtZW5kIC0tPlxuIn0= -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucmVzdWx0c091dEFic0RpZmZzJE9PQl9NQUVcbmBgYFxuYGBgIn0= -->

```r
```r
resultsOutAbsDiffs$OOB_MAE
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDAuNDA3Mzc4N1xuXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVjbVZ6ZFd4MGMwOTFkRUZpYzBScFptWnpKRkp6Y1hWaGNtVmtYRzVnWUdBaWZRPT0gLS0+XG5cbmBgYHJcbnJlc3VsdHNPdXRBYnNEaWZmcyRSc3F1YXJlZFxuYGBgXG5cbjwhLS0gcm5iLXNvdXJjZS1lbmQgLS0+XG4ifQ== -->

[1] 0.4073787

resultsOutAbsDiffs$Rsquared



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dWNtVnpkV3gwYzA5MWRFRmljMFJwWm1aekpGSnpjWFZoY21Wa1hHNWdZR0JjYm1CZ1lDSjkgLS0+XG5cbmBgYHJcbmBgYHJcbnJlc3VsdHNPdXRBYnNEaWZmcyRSc3F1YXJlZFxuYGBgXG5gYGBcblxuPCEtLSBybmItc291cmNlLWVuZCAtLT5cbiJ9 -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucmVzdWx0c091dEFic0RpZmZzJFJzcXVhcmVkXG5gYGBcbmBgYCJ9 -->

```r
```r
resultsOutAbsDiffs$Rsquared
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDAuODkyMTc4M1xuIn0= -->

```
[1] 0.8921783
```



<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->



<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->
# store results
resdAnnoyMnFitAB['All vars', 'RMSE'] <- resultsOutAbsDiffs$OOB_RMSE
resdAnnoyMnFitAB['All vars', 'MAE'] <- resultsOutAbsDiffs$OOB_MAE
resdAnnoyMnFitAB['All vars', 'Rsquared'] <- resultsOutAbsDiffs$Rsquared
resdAnnoyMnPermImpAB$AllVars <- resultsOutAbsDiffs$conditional_permimp



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


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```r
```r
# store results
resdAnnoyMnFitAB['All vars', 'RMSE'] <- resultsOutAbsDiffs$OOB_RMSE
resdAnnoyMnFitAB['All vars', 'MAE'] <- resultsOutAbsDiffs$OOB_MAE
resdAnnoyMnFitAB['All vars', 'Rsquared'] <- resultsOutAbsDiffs$Rsquared
resdAnnoyMnPermImpAB$AllVars <- resultsOutAbsDiffs$conditional_permimp

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


#### Plot results


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutAbsDiffs.conimp <- arrange(resultsOutAbsDiffs$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutAbsDiffs.conimp) + geom_col(aes(x=factor(rownames(resultsOutAbsDiffs.conimp), levels=rownames(resultsOutAbsDiffs.conimp)), y=CondPermImp), fill=mycolours[9], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (mean change in annoyance)\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar



<!-- rnb-output-end -->

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```r
```r
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutAbsDiffs.conimp <- arrange(resultsOutAbsDiffs$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutAbsDiffs.conimp) + geom_col(aes(x=factor(rownames(resultsOutAbsDiffs.conimp), levels=rownames(resultsOutAbsDiffs.conimp)), y=CondPermImp), fill=mycolours[9], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (mean change in annoyance)\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar
```
```

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```

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<img src=\data:image/png;base64
```



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\" />\n\n<!-- rnb-plot-end -->\n"} -->


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```r
```r

if (saveplots){
  ggsave(filename=\PtsABdAnnoyMnAllVarsConPermimp.svg\, width=8, height=20, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdAnnoyMnAllVarsConPermimp.svg\)

  ggsave(filename=\PtsABdAnnoyMnAllVarsConPermimp.pdf\, width=8, height=20, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdAnnoyMnAllVarsConPermimp.pdf\)
}

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->



<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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# Plot only positive values

resultsOutAbsDiffs.conimpPtv <- resultsOutAbsDiffs.conimp |>
                                          rownames_to_column('Metric') |>
                                                filter_if(is.numeric, all_vars(. > 0)) |>
                                                      column_to_rownames('Metric')

pBar <- ggplot(resultsOutAbsDiffs.conimpPtv) + geom_col(aes(x=factor(rownames(resultsOutAbsDiffs.conimpPtv), levels=rownames(resultsOutAbsDiffs.conimpPtv)), y=CondPermImp), fill=mycolours[9], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (mean change in annoyance)\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar



<!-- rnb-output-end -->

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```r
```r

# Plot only positive values

resultsOutAbsDiffs.conimpPtv <- resultsOutAbsDiffs.conimp |>
                                          rownames_to_column('Metric') |>
                                                filter_if(is.numeric, all_vars(. > 0)) |>
                                                      column_to_rownames('Metric')

pBar <- ggplot(resultsOutAbsDiffs.conimpPtv) + geom_col(aes(x=factor(rownames(resultsOutAbsDiffs.conimpPtv), levels=rownames(resultsOutAbsDiffs.conimpPtv)), y=CondPermImp), fill=mycolours[9], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (mean change in annoyance)\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

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```

<!-- rnb-plot-begin eyJoZWlnaHQiOjEwLCJ3aWR0aCI6OCwic2l6ZV9iZWhhdmlvciI6MSwiY29uZGl0aW9ucyI6W119 -->

<img src=\data:image/png;base64
```



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\" 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" />

<!-- rnb-plot-end -->


<!-- rnb-output-end -->

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if (saveplots){
  ggsave(filename=\PtsABdAnnoyMnAllVarsConPermimpPtv.svg\, width=8, height=10, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdAnnoyMnAllVarsConPermimp.svg\)
  
  ggsave(filename=\PtsABdAnnoyMnAllVarsConPermimpPtv.pdf\, width=8, height=10, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdAnnoyMnAllVarsConPermimp.pdf\)
}



<!-- rnb-output-end -->

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```r
```r

if (saveplots){
  ggsave(filename=\PtsABdAnnoyMnAllVarsConPermimpPtv.svg\, width=8, height=10, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdAnnoyMnAllVarsConPermimp.svg\)
  
  ggsave(filename=\PtsABdAnnoyMnAllVarsConPermimpPtv.pdf\, width=8, height=10, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdAnnoyMnAllVarsConPermimp.pdf\)
}

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

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<!-- rnb-chunk-begin -->


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# Plot only values within 1% of the maximum

resultsOutAbsDiffs.conimp1pc <- resultsOutAbsDiffs.conimp |>
                                          rownames_to_column('Metric') |>
                                                filter_if(is.numeric, all_vars(. > max(resultsOutAbsDiffs.conimp)/100)) |>
                                                      column_to_rownames('Metric')

pBar <- ggplot(resultsOutAbsDiffs.conimp1pc) + geom_col(aes(x=factor(rownames(resultsOutAbsDiffs.conimp1pc), levels=rownames(resultsOutAbsDiffs.conimp1pc)), y=CondPermImp), fill=mycolours[9], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (mean change in annoyance)\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar



<!-- rnb-output-end -->

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```r
```r

# Plot only values within 1% of the maximum

resultsOutAbsDiffs.conimp1pc <- resultsOutAbsDiffs.conimp |>
                                          rownames_to_column('Metric') |>
                                                filter_if(is.numeric, all_vars(. > max(resultsOutAbsDiffs.conimp)/100)) |>
                                                      column_to_rownames('Metric')

pBar <- ggplot(resultsOutAbsDiffs.conimp1pc) + geom_col(aes(x=factor(rownames(resultsOutAbsDiffs.conimp1pc), levels=rownames(resultsOutAbsDiffs.conimp1pc)), y=CondPermImp), fill=mycolours[9], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (mean change in annoyance)\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar
```
```

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<!-- rnb-output-end -->

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```

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<img src=\data:image/png;base64
```



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<!-- rnb-output-end -->

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  unlink(\PtsABdAnnoyMnAllVarsConPermimp1pc.pdf\)
}



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


<!-- rnb-source-begin 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 -->

```r
```r

if (saveplots){
  ggsave(filename=\PtsABdAnnoyMnAllVarsConPermimp1pc.svg\, width=8, height=4, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdAnnoyMnAllVarsConPermimp1pc.svg\)
  
  ggsave(filename=\PtsABdAnnoyMnAllVarsConPermimp1pc.pdf\, width=8, height=4, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdAnnoyMnAllVarsConPermimp1pc.pdf\)
}

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->



### Absolute variables

#### Set variables


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->

iVars <- names(stimDataNum)[which(names(stimDataNum) == 'UASEvents'):which(names(stimDataNum) == 'UASImpulsSHM05ExMaxLR')]
iVars <- iVars[! iVars %in% c('SNRlevel', 'IntermitRatioMaxLR')]
dVar <- \dAnnoyMean\

seeds <- c(578312, 544, 84894, 54654, 153157)



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


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```r
```r

iVars <- names(stimDataNum)[which(names(stimDataNum) == 'UASEvents'):which(names(stimDataNum) == 'UASImpulsSHM05ExMaxLR')]
iVars <- iVars[! iVars %in% c('SNRlevel', 'IntermitRatioMaxLR')]
dVar <- \dAnnoyMean\

seeds <- c(578312, 544, 84894, 54654, 153157)

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


#### Hyperparameter tuning


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
              ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5wIDwtIG10cnlUdW5lKGRhdGFJbj1zdGltRGF0YU51bSwgaVZhcnM9aVZhcnMsIGRWYXI9ZFZhciwgc2VlZD1zZWVkc1sxOjJdLFxuICAgICAgICAgICAgICBudHJlZXM9bnRyZWVzLCBtaW5zcGxpdD1taW5zcGxpdCwgbWluYnVja2V0PW1pbmJ1Y2tldClcbmBgYFxuYGBgIn0= -->

```r
```r

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
              ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

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\" 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" />

<!-- rnb-plot-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVYRzVwWmlBb2MyRjJaWEJzYjNSektYdGNiaUFnWjJkellYWmxLR1pwYkdWdVlXMWxQVndpVUhSelFVSmtRVzV1YjNsTmJrRmljMVpoY25OSWVYQmxjbFIxYm1VdWMzWm5YQ0lzSUhkcFpIUm9QVEV5TENCb1pXbG5hSFE5TkN3Z2NHRjBhRDFtYVd4bExuQmhkR2dvYjNWMFJtbG5VR0YwYUN3Z1hDSnpkbWRjSWlrcFhHNGdJSFZ1YkdsdWF5aGNJbEIwYzBGQ1pFRnVibTk1VFc1QlluTldZWEp6U0hsd1pYSlVkVzVsTG5OMloxd2lLVnh1WEc0Z0lHZG5jMkYyWlNobWFXeGxibUZ0WlQxY0lsQjBjMEZDWkVGdWJtOTVUVzVCWW5OV1lYSnpTSGx3WlhKVWRXNWxMbkJrWmx3aUxDQjNhV1IwYUQweE1pd2dhR1ZwWjJoMFBUUXNJSEJoZEdnOVptbHNaUzV3WVhSb0tHOTFkRVpwWjFCaGRHZ3NJRndpY0dSbVhDSXBLVnh1SUNCMWJteHBibXNvWENKUWRITkJRbVJCYm01dmVVMXVRV0p6Vm1GeWMwaDVjR1Z5VkhWdVpTNXdaR1pjSWlsY2JuMWNibHh1WUdCZ0luMD0gLS0+XG5cbmBgYHJcblxuaWYgKHNhdmVwbG90cyl7XG4gIGdnc2F2ZShmaWxlbmFtZT1cXFB0c0FCZEFubm95TW5BYnNWYXJzSHlwZXJUdW5lLnN2Z1xcLCB3aWR0aD0xMiwgaGVpZ2h0PTQsIHBhdGg9ZmlsZS5wYXRoKG91dEZpZ1BhdGgsIFxcc3ZnXFwpKVxuICB1bmxpbmsoXFxQdHNBQmRBbm5veU1uQWJzVmFyc0h5cGVyVHVuZS5zdmdcXClcblxuICBnZ3NhdmUoZmlsZW5hbWU9XFxQdHNBQmRBbm5veU1uQWJzVmFyc0h5cGVyVHVuZS5wZGZcXCwgd2lkdGg9MTIsIGhlaWdodD00LCBwYXRoPWZpbGUucGF0aChvdXRGaWdQYXRoLCBcXHBkZlxcKSlcbiAgdW5saW5rKFxcUHRzQUJkQW5ub3lNbkFic1ZhcnNIeXBlclR1bmUucGRmXFwpXG59XG5cbmBgYFxuXG48IS0tIHJuYi1zb3VyY2UtZW5kIC0tPlxuIn0= -->

if (saveplots){
  ggsave(filename=\PtsABdAnnoyMnAbsVarsHyperTune.svg\, width=12, height=4, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdAnnoyMnAbsVarsHyperTune.svg\)

  ggsave(filename=\PtsABdAnnoyMnAbsVarsHyperTune.pdf\, width=12, height=4, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdAnnoyMnAbsVarsHyperTune.pdf\)
}



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


<!-- rnb-source-begin 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 -->

```r
```r

if (saveplots){
  ggsave(filename=\PtsABdAnnoyMnAbsVarsHyperTune.svg\, width=12, height=4, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdAnnoyMnAbsVarsHyperTune.svg\)

  ggsave(filename=\PtsABdAnnoyMnAbsVarsHyperTune.pdf\, width=12, height=4, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdAnnoyMnAbsVarsHyperTune.pdf\)
}

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Selected hyperparameters


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVYRzV1ZEhKbFpTQThMU0F5TlRBeFhHNXRkSEo1SUR3dElHRnpMbWx1ZEdWblpYSW9iR1Z1WjNSb0tHbFdZWEp6S1M4eExqSTFLVnh1WEc1Z1lHQWlmUT09IC0tPlxuXG5gYGByXG5cbm50cmVlIDwtIDI1MDFcbm10cnkgPC0gYXMuaW50ZWdlcihsZW5ndGgoaVZhcnMpLzEuMjUpXG5cbmBgYFxuXG48IS0tIHJuYi1zb3VyY2UtZW5kIC0tPlxuIn0= -->

ntree <- 2501
mtry <- as.integer(length(iVars)/1.25)



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dVhHNXVkSEpsWlNBOExTQXlOVEF4WEc1dGRISjVJRHd0SUdGekxtbHVkR1ZuWlhJb2JHVnVaM1JvS0dsV1lYSnpLUzh4TGpJMUtWeHVYRzVnWUdCY2JtQmdZQ0o5IC0tPlxuXG5gYGByXG5gYGByXG5cbm50cmVlIDwtIDI1MDFcbm10cnkgPC0gYXMuaW50ZWdlcihsZW5ndGgoaVZhcnMpLzEuMjUpXG5cbmBgYFxuYGBgXG5cbjwhLS0gcm5iLXNvdXJjZS1lbmQgLS0+XG4ifQ== -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5udHJlZSA8LSAyNTAxXG5tdHJ5IDwtIGFzLmludGVnZXIobGVuZ3RoKGlWYXJzKS8xLjI1KVxuXG5gYGBcbmBgYCJ9 -->

```r
```r

ntree <- 2501
mtry <- as.integer(length(iVars)/1.25)

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


#### Run model

Train preliminary model


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->

nperm <- 15

resultsOutAbs <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutAbs$OOB_RMSE



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5ucGVybSA8LSAxNVxuXG5yZXN1bHRzT3V0QWJzIDwtIGNyZlJlZyhkYXRhSW49c3RpbURhdGFOdW0sIGlWYXJzPWlWYXJzLCBkVmFyPWRWYXIsIHNlZWRzPXNlZWRzWzE6Ml0sIG50cmVlPW50cmVlLCBtdHJ5PW10cnksIHBlcm1JbXBDb25kVGhyZXM9cGVybUltcENvbmRUaHJlcywgbnBlcm09bnBlcm0sIG1pbnNwbGl0PW1pbnNwbGl0LCBtaW5idWNrZXQ9bWluYnVja2V0KVxuXG4jIHByaW50IG1vZGVsIHByZWRpY3Rpb24gcmVzdWx0c1xucmVzdWx0c091dEFicyRPT0JfUk1TRVxuYGBgXG5gYGAifQ== -->

```r
```r

nperm <- 15

resultsOutAbs <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutAbs$OOB_RMSE
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDAuNzA4ODExOFxuXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVjbVZ6ZFd4MGMwOTFkRUZpY3lSUFQwSmZUVUZGWEc1Z1lHQWlmUT09IC0tPlxuXG5gYGByXG5yZXN1bHRzT3V0QWJzJE9PQl9NQUVcbmBgYFxuXG48IS0tIHJuYi1zb3VyY2UtZW5kIC0tPlxuIn0= -->

[1] 0.7088118

resultsOutAbs$OOB_MAE



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dWNtVnpkV3gwYzA5MWRFRmljeVJQVDBKZlRVRkZYRzVnWUdCY2JtQmdZQ0o5IC0tPlxuXG5gYGByXG5gYGByXG5yZXN1bHRzT3V0QWJzJE9PQl9NQUVcbmBgYFxuYGBgXG5cbjwhLS0gcm5iLXNvdXJjZS1lbmQgLS0+XG4ifQ== -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucmVzdWx0c091dEFicyRPT0JfTUFFXG5gYGBcbmBgYCJ9 -->

```r
```r
resultsOutAbs$OOB_MAE
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDAuNTQxMTQ4OFxuXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVjbVZ6ZFd4MGMwOTFkRUZpY3lSU2MzRjFZWEpsWkZ4dVlHQmdJbjA9IC0tPlxuXG5gYGByXG5yZXN1bHRzT3V0QWJzJFJzcXVhcmVkXG5gYGBcblxuPCEtLSBybmItc291cmNlLWVuZCAtLT5cbiJ9 -->

[1] 0.5411488

resultsOutAbs$Rsquared



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dWNtVnpkV3gwYzA5MWRFRmljeVJTYzNGMVlYSmxaRnh1WUdCZ1hHNWdZR0FpZlE9PSAtLT5cblxuYGBgclxuYGBgclxucmVzdWx0c091dEFicyRSc3F1YXJlZFxuYGBgXG5gYGBcblxuPCEtLSBybmItc291cmNlLWVuZCAtLT5cbiJ9 -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucmVzdWx0c091dEFicyRSc3F1YXJlZFxuYGBgXG5gYGAifQ== -->

```r
```r
resultsOutAbs$Rsquared
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDAuODEzNTMyMlxuIn0= -->

```
[1] 0.8135322
```



<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->

Train multiple seeds model


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->

resultsOutAbs <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutAbs$OOB_RMSE



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5yZXN1bHRzT3V0QWJzIDwtIG11bHRpX2NyZlJlZyhkYXRhSW49c3RpbURhdGFOdW0sIGlWYXJzPWlWYXJzLCBkVmFyPWRWYXIsIHNlZWRzPXNlZWRzLCBudHJlZT1udHJlZSwgbXRyeT1tdHJ5LCBwZXJtSW1wQ29uZFRocmVzPXBlcm1JbXBDb25kVGhyZXMsIG5wZXJtPW5wZXJtLCBtaW5zcGxpdD1taW5zcGxpdCwgbWluYnVja2V0PW1pbmJ1Y2tldClcblxuIyBwcmludCBtb2RlbCBwcmVkaWN0aW9uIHJlc3VsdHNcbnJlc3VsdHNPdXRBYnMkT09CX1JNU0VcbmBgYFxuYGBgIn0= -->

```r
```r

resultsOutAbs <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutAbs$OOB_RMSE
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDAuNzA3NjU4NVxuXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVjbVZ6ZFd4MGMwOTFkRUZpY3lSUFQwSmZUVUZGWEc1Z1lHQWlmUT09IC0tPlxuXG5gYGByXG5yZXN1bHRzT3V0QWJzJE9PQl9NQUVcbmBgYFxuXG48IS0tIHJuYi1zb3VyY2UtZW5kIC0tPlxuIn0= -->

[1] 0.7076585

resultsOutAbs$OOB_MAE



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dWNtVnpkV3gwYzA5MWRFRmljeVJQVDBKZlRVRkZYRzVnWUdCY2JtQmdZQ0o5IC0tPlxuXG5gYGByXG5gYGByXG5yZXN1bHRzT3V0QWJzJE9PQl9NQUVcbmBgYFxuYGBgXG5cbjwhLS0gcm5iLXNvdXJjZS1lbmQgLS0+XG4ifQ== -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucmVzdWx0c091dEFicyRPT0JfTUFFXG5gYGBcbmBgYCJ9 -->

```r
```r
resultsOutAbs$OOB_MAE
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDAuNTQwOTcwNVxuXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVjbVZ6ZFd4MGMwOTFkRUZpY3lSU2MzRjFZWEpsWkZ4dVlHQmdJbjA9IC0tPlxuXG5gYGByXG5yZXN1bHRzT3V0QWJzJFJzcXVhcmVkXG5gYGBcblxuPCEtLSBybmItc291cmNlLWVuZCAtLT5cbiJ9 -->

[1] 0.5409705

resultsOutAbs$Rsquared



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dWNtVnpkV3gwYzA5MWRFRmljeVJTYzNGMVlYSmxaRnh1WUdCZ1hHNWdZR0FpZlE9PSAtLT5cblxuYGBgclxuYGBgclxucmVzdWx0c091dEFicyRSc3F1YXJlZFxuYGBgXG5gYGBcblxuPCEtLSBybmItc291cmNlLWVuZCAtLT5cbiJ9 -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucmVzdWx0c091dEFicyRSc3F1YXJlZFxuYGBgXG5gYGAifQ== -->

```r
```r
resultsOutAbs$Rsquared
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDAuODEzODE5XG4ifQ== -->

```
[1] 0.813819
```



<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->




<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->

# store results
resdAnnoyMnFitAB['Abs vars', 'RMSE'] <- resultsOutAbs$OOB_RMSE
resdAnnoyMnFitAB['Abs vars', 'MAE'] <- resultsOutAbs$OOB_MAE
resdAnnoyMnFitAB['Abs vars', 'Rsquared'] <- resultsOutAbs$Rsquared
resdAnnoyMnPermImpAB$AbsVars <- resultsOutAbs$conditional_permimp



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


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```r
```r

# store results
resdAnnoyMnFitAB['Abs vars', 'RMSE'] <- resultsOutAbs$OOB_RMSE
resdAnnoyMnFitAB['Abs vars', 'MAE'] <- resultsOutAbs$OOB_MAE
resdAnnoyMnFitAB['Abs vars', 'Rsquared'] <- resultsOutAbs$Rsquared
resdAnnoyMnPermImpAB$AbsVars <- resultsOutAbs$conditional_permimp

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


#### Plot results


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutAbs.conimp <- arrange(resultsOutAbs$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutAbs.conimp) + geom_col(aes(x=factor(rownames(resultsOutAbs.conimp), levels=rownames(resultsOutAbs.conimp)), y=CondPermImp), fill=mycolours[1], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (mean change in annoyance)\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) +
  coord_flip()
pBar



<!-- rnb-output-end -->

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```r
```r
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutAbs.conimp <- arrange(resultsOutAbs$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutAbs.conimp) + geom_col(aes(x=factor(rownames(resultsOutAbs.conimp), levels=rownames(resultsOutAbs.conimp)), y=CondPermImp), fill=mycolours[1], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (mean change in annoyance)\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) +
  coord_flip()
pBar
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

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```

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<img src=\data:image/png;base64
```



<!-- rnb-output-end -->

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\" 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/>

<!-- rnb-plot-end -->


<!-- rnb-output-end -->

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if (saveplots){
  ggsave(filename=\PtsABdAnnoyMnAbsVarsConPermimp.svg\, width=8, height=11.5, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdAnnoyMnAbsVarsConPermimp.svg\)
  
  ggsave(filename=\PtsABdAnnoyMnAbsVarsConPermimp.pdf\, width=8, height=11.5, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdAnnoyMnAbsVarsConPermimp.pdf\)
}



<!-- rnb-output-end -->

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```r
```r

if (saveplots){
  ggsave(filename=\PtsABdAnnoyMnAbsVarsConPermimp.svg\, width=8, height=11.5, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdAnnoyMnAbsVarsConPermimp.svg\)
  
  ggsave(filename=\PtsABdAnnoyMnAbsVarsConPermimp.pdf\, width=8, height=11.5, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdAnnoyMnAbsVarsConPermimp.pdf\)
}
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->




<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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# Plot only positive values

resultsOutAbs.conimpPtv <- resultsOutAbs.conimp |>
                                          rownames_to_column('Metric') |>
                                                filter_if(is.numeric, all_vars(. > 0)) |>
                                                      column_to_rownames('Metric')

pBar <- ggplot(resultsOutAbs.conimpPtv) + geom_col(aes(x=factor(rownames(resultsOutAbs.conimpPtv), levels=rownames(resultsOutAbs.conimpPtv)), y=CondPermImp), fill=mycolours[1], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (mean change in annoyance)\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar



<!-- rnb-output-end -->

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```r
```r

# Plot only positive values

resultsOutAbs.conimpPtv <- resultsOutAbs.conimp |>
                                          rownames_to_column('Metric') |>
                                                filter_if(is.numeric, all_vars(. > 0)) |>
                                                      column_to_rownames('Metric')

pBar <- ggplot(resultsOutAbs.conimpPtv) + geom_col(aes(x=factor(rownames(resultsOutAbs.conimpPtv), levels=rownames(resultsOutAbs.conimpPtv)), y=CondPermImp), fill=mycolours[1], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (mean change in annoyance)\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar
```
```

<!-- rnb-source-end -->


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```

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<img src=\data:image/png;base64
```



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" />

<!-- rnb-plot-end -->


<!-- rnb-output-end -->

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if (saveplots){
  ggsave(filename=\PtsABdAnnoyMnAbsVarsConPermimpPtv.svg\, width=8, height=8, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdAnnoyMnAbsVarsConPermimpPtv.svg\)
  
  ggsave(filename=\PtsABdAnnoyMnAbsVarsConPermimpPtv.pdf\, width=8, height=8, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdAnnoyMnAbsVarsConPermimpPtv.pdf\)
}



<!-- rnb-output-end -->

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```r
```r

if (saveplots){
  ggsave(filename=\PtsABdAnnoyMnAbsVarsConPermimpPtv.svg\, width=8, height=8, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdAnnoyMnAbsVarsConPermimpPtv.svg\)
  
  ggsave(filename=\PtsABdAnnoyMnAbsVarsConPermimpPtv.pdf\, width=8, height=8, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdAnnoyMnAbsVarsConPermimpPtv.pdf\)
}

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->



<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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# Plot only values within 1% of the maximum

resultsOutAbs.conimp1pc <- resultsOutAbs.conimp |>
                                          rownames_to_column('Metric') |>
                                                filter_if(is.numeric, all_vars(. > max(resultsOutAbs.conimp)/100)) |>
                                                      column_to_rownames('Metric')

pBar <- ggplot(resultsOutAbs.conimp1pc) + geom_col(aes(x=factor(rownames(resultsOutAbs.conimp1pc), levels=rownames(resultsOutAbs.conimp1pc)), y=CondPermImp), fill=mycolours[1], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (mean change in annoyance)\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar



<!-- rnb-output-end -->

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```r
```r

# Plot only values within 1% of the maximum

resultsOutAbs.conimp1pc <- resultsOutAbs.conimp |>
                                          rownames_to_column('Metric') |>
                                                filter_if(is.numeric, all_vars(. > max(resultsOutAbs.conimp)/100)) |>
                                                      column_to_rownames('Metric')

pBar <- ggplot(resultsOutAbs.conimp1pc) + geom_col(aes(x=factor(rownames(resultsOutAbs.conimp1pc), levels=rownames(resultsOutAbs.conimp1pc)), y=CondPermImp), fill=mycolours[1], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (mean change in annoyance)\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

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<img src=\data:image/png;base64
```



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if (saveplots){
  ggsave(filename=\PtsABdAnnoyMnAbsVarsConPermimp1pc.svg\, width=8, height=2.6, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdAnnoyMnAbsVarsConPermimp1pc.svg\)
  
  ggsave(filename=\PtsABdAnnoyMnAbsVarsConPermimp1pc.pdf\, width=8, height=2.6, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdAnnoyMnAbsVarsConPermimp1pc.pdf\)
}



<!-- rnb-output-end -->

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```r
```r

if (saveplots){
  ggsave(filename=\PtsABdAnnoyMnAbsVarsConPermimp1pc.svg\, width=8, height=2.6, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdAnnoyMnAbsVarsConPermimp1pc.svg\)
  
  ggsave(filename=\PtsABdAnnoyMnAbsVarsConPermimp1pc.pdf\, width=8, height=2.6, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdAnnoyMnAbsVarsConPermimp1pc.pdf\)
}

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Selected metric


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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absVar <- \UASLAEMaxLR\



<!-- rnb-output-end -->

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```r
```r

absVar <- \UASLAEMaxLR\

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->



### SQM analysis

#### Replace ambient only stimuli

The ambient-only stimuli are NOT replaced here, as the analysis cannot handle missing values for dB metrics.

#### Individual SQMs

##### Sharpness

###### Set variables


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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iVars <- c(absVar, eventVar, ambVar, \UASSharpAurISO3PowAvgMaxLR\, \UASSharpAurISO305ExMaxLR\, \UASSharpAurSHMPowAvgMaxLR\, \UASSharpAurSHM05ExMaxLR\, \UASSharpAurISO1PowAvgMaxLR\, \UASSharpAurISO105ExMaxLR\, \UASSharpvBISO1PowAvgMaxLR\, \UASSharpvBISO105ExMaxLR\, \UASSharpDINPowAvgMaxLR\, \UASSharpDIN05ExMaxLR\)
dVar <- \dAnnoyMean\

seeds <- c(7041, 905, 4984651, 6513213, 120651)



<!-- rnb-output-end -->

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```r
```r

iVars <- c(absVar, eventVar, ambVar, \UASSharpAurISO3PowAvgMaxLR\, \UASSharpAurISO305ExMaxLR\, \UASSharpAurSHMPowAvgMaxLR\, \UASSharpAurSHM05ExMaxLR\, \UASSharpAurISO1PowAvgMaxLR\, \UASSharpAurISO105ExMaxLR\, \UASSharpvBISO1PowAvgMaxLR\, \UASSharpvBISO105ExMaxLR\, \UASSharpDINPowAvgMaxLR\, \UASSharpDIN05ExMaxLR\)
dVar <- \dAnnoyMean\

seeds <- c(7041, 905, 4984651, 6513213, 120651)

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


###### Hyperparameter tuning


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)



<!-- rnb-output-end -->

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```r
```r

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

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\" 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" />

<!-- rnb-plot-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Selected hyperparameters


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVYRzV1ZEhKbFpTQThMU0F5TlRGY2JtMTBjbmtnUEMwZ1lYTXVhVzUwWldkbGNpaHNaVzVuZEdnb2FWWmhjbk1wTHpFdU1qVXBYRzVjYm1CZ1lDSjkgLS0+XG5cbmBgYHJcblxubnRyZWUgPC0gMjUxXG5tdHJ5IDwtIGFzLmludGVnZXIobGVuZ3RoKGlWYXJzKS8xLjI1KVxuXG5gYGBcblxuPCEtLSBybmItc291cmNlLWVuZCAtLT5cbiJ9 -->

ntree <- 251
mtry <- as.integer(length(iVars)/1.25)



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dVhHNXVkSEpsWlNBOExTQXlOVEZjYm0xMGNua2dQQzBnWVhNdWFXNTBaV2RsY2loc1pXNW5kR2dvYVZaaGNuTXBMekV1TWpVcFhHNWNibUJnWUZ4dVlHQmdJbjA9IC0tPlxuXG5gYGByXG5gYGByXG5cbm50cmVlIDwtIDI1MVxubXRyeSA8LSBhcy5pbnRlZ2VyKGxlbmd0aChpVmFycykvMS4yNSlcblxuYGBgXG5gYGBcblxuPCEtLSBybmItc291cmNlLWVuZCAtLT5cbiJ9 -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5udHJlZSA8LSAyNTFcbm10cnkgPC0gYXMuaW50ZWdlcihsZW5ndGgoaVZhcnMpLzEuMjUpXG5cbmBgYFxuYGBgIn0= -->

```r
```r

ntree <- 251
mtry <- as.integer(length(iVars)/1.25)

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


###### Run model

Train preliminary model


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVYRzV1Y0dWeWJTQThMU0ExWEc1Y2JuSmxjM1ZzZEhOUGRYUlRhR0Z5Y0NBOExTQmpjbVpTWldjb1pHRjBZVWx1UFhOMGFXMUVZWFJoVG5WdExDQnBWbUZ5Y3oxcFZtRnljeXdnWkZaaGNqMWtWbUZ5TENCelpXVmtjejF6WldWa2Mxc3hPakpkTENCdWRISmxaVDF1ZEhKbFpTd2diWFJ5ZVQxdGRISjVMQ0J3WlhKdFNXMXdRMjl1WkZSb2NtVnpQWEJsY20xSmJYQkRiMjVrVkdoeVpYTXNJRzV3WlhKdFBXNXdaWEp0TENCdGFXNXpjR3hwZEQxdGFXNXpjR3hwZEN3Z2JXbHVZblZqYTJWMFBXMXBibUoxWTJ0bGRDbGNibHh1SXlCd2NtbHVkQ0J0YjJSbGJDQndjbVZrYVdOMGFXOXVJSEpsYzNWc2RITmNibkpsYzNWc2RITlBkWFJUYUdGeWNDUlBUMEpmVWsxVFJWeHVZR0JnSW4wPSAtLT5cblxuYGBgclxuXG5ucGVybSA8LSA1XG5cbnJlc3VsdHNPdXRTaGFycCA8LSBjcmZSZWcoZGF0YUluPXN0aW1EYXRhTnVtLCBpVmFycz1pVmFycywgZFZhcj1kVmFyLCBzZWVkcz1zZWVkc1sxOjJdLCBudHJlZT1udHJlZSwgbXRyeT1tdHJ5LCBwZXJtSW1wQ29uZFRocmVzPXBlcm1JbXBDb25kVGhyZXMsIG5wZXJtPW5wZXJtLCBtaW5zcGxpdD1taW5zcGxpdCwgbWluYnVja2V0PW1pbmJ1Y2tldClcblxuIyBwcmludCBtb2RlbCBwcmVkaWN0aW9uIHJlc3VsdHNcbnJlc3VsdHNPdXRTaGFycCRPT0JfUk1TRVxuYGBgXG5cbjwhLS0gcm5iLXNvdXJjZS1lbmQgLS0+XG4ifQ== -->

nperm <- 5

resultsOutSharp <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSharp$OOB_RMSE



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dVhHNXVjR1Z5YlNBOExTQTFYRzVjYm5KbGMzVnNkSE5QZFhSVGFHRnljQ0E4TFNCamNtWlNaV2NvWkdGMFlVbHVQWE4wYVcxRVlYUmhUblZ0TENCcFZtRnljejFwVm1GeWN5d2daRlpoY2oxa1ZtRnlMQ0J6WldWa2N6MXpaV1ZrYzFzeE9qSmRMQ0J1ZEhKbFpUMXVkSEpsWlN3Z2JYUnllVDF0ZEhKNUxDQndaWEp0U1cxd1EyOXVaRlJvY21WelBYQmxjbTFKYlhCRGIyNWtWR2h5WlhNc0lHNXdaWEp0UFc1d1pYSnRMQ0J0YVc1emNHeHBkRDF0YVc1emNHeHBkQ3dnYldsdVluVmphMlYwUFcxcGJtSjFZMnRsZENsY2JseHVJeUJ3Y21sdWRDQnRiMlJsYkNCd2NtVmthV04wYVc5dUlISmxjM1ZzZEhOY2JuSmxjM1ZzZEhOUGRYUlRhR0Z5Y0NSUFQwSmZVazFUUlZ4dVlHQmdYRzVnWUdBaWZRPT0gLS0+XG5cbmBgYHJcbmBgYHJcblxubnBlcm0gPC0gNVxuXG5yZXN1bHRzT3V0U2hhcnAgPC0gY3JmUmVnKGRhdGFJbj1zdGltRGF0YU51bSwgaVZhcnM9aVZhcnMsIGRWYXI9ZFZhciwgc2VlZHM9c2VlZHNbMToyXSwgbnRyZWU9bnRyZWUsIG10cnk9bXRyeSwgcGVybUltcENvbmRUaHJlcz1wZXJtSW1wQ29uZFRocmVzLCBucGVybT1ucGVybSwgbWluc3BsaXQ9bWluc3BsaXQsIG1pbmJ1Y2tldD1taW5idWNrZXQpXG5cbiMgcHJpbnQgbW9kZWwgcHJlZGljdGlvbiByZXN1bHRzXG5yZXN1bHRzT3V0U2hhcnAkT09CX1JNU0VcbmBgYFxuYGBgXG5cbjwhLS0gcm5iLXNvdXJjZS1lbmQgLS0+XG4ifQ== -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5ucGVybSA8LSA1XG5cbnJlc3VsdHNPdXRTaGFycCA8LSBjcmZSZWcoZGF0YUluPXN0aW1EYXRhTnVtLCBpVmFycz1pVmFycywgZFZhcj1kVmFyLCBzZWVkcz1zZWVkc1sxOjJdLCBudHJlZT1udHJlZSwgbXRyeT1tdHJ5LCBwZXJtSW1wQ29uZFRocmVzPXBlcm1JbXBDb25kVGhyZXMsIG5wZXJtPW5wZXJtLCBtaW5zcGxpdD1taW5zcGxpdCwgbWluYnVja2V0PW1pbmJ1Y2tldClcblxuIyBwcmludCBtb2RlbCBwcmVkaWN0aW9uIHJlc3VsdHNcbnJlc3VsdHNPdXRTaGFycCRPT0JfUk1TRVxuYGBgXG5gYGAifQ== -->

```r
```r

nperm <- 5

resultsOutSharp <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSharp$OOB_RMSE
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDAuNjg1ODQ5MlxuXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVjbVZ6ZFd4MGMwOTFkRk5vWVhKd0pFOVBRbDlOUVVWY2JtQmdZQ0o5IC0tPlxuXG5gYGByXG5yZXN1bHRzT3V0U2hhcnAkT09CX01BRVxuYGBgXG5cbjwhLS0gcm5iLXNvdXJjZS1lbmQgLS0+XG4ifQ== -->

[1] 0.6858492

resultsOutSharp$OOB_MAE



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dWNtVnpkV3gwYzA5MWRGTm9ZWEp3SkU5UFFsOU5RVVZjYm1CZ1lGeHVZR0JnSW4wPSAtLT5cblxuYGBgclxuYGBgclxucmVzdWx0c091dFNoYXJwJE9PQl9NQUVcbmBgYFxuYGBgXG5cbjwhLS0gcm5iLXNvdXJjZS1lbmQgLS0+XG4ifQ== -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucmVzdWx0c091dFNoYXJwJE9PQl9NQUVcbmBgYFxuYGBgIn0= -->

```r
```r
resultsOutSharp$OOB_MAE
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDAuNTI4MDc1N1xuXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVjbVZ6ZFd4MGMwOTFkRk5vWVhKd0pGSnpjWFZoY21Wa1hHNWdZR0FpZlE9PSAtLT5cblxuYGBgclxucmVzdWx0c091dFNoYXJwJFJzcXVhcmVkXG5gYGBcblxuPCEtLSBybmItc291cmNlLWVuZCAtLT5cbiJ9 -->

[1] 0.5280757

resultsOutSharp$Rsquared



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dWNtVnpkV3gwYzA5MWRGTm9ZWEp3SkZKemNYVmhjbVZrWEc1Z1lHQmNibUJnWUNKOSAtLT5cblxuYGBgclxuYGBgclxucmVzdWx0c091dFNoYXJwJFJzcXVhcmVkXG5gYGBcbmBgYFxuXG48IS0tIHJuYi1zb3VyY2UtZW5kIC0tPlxuIn0= -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucmVzdWx0c091dFNoYXJwJFJzcXVhcmVkXG5gYGBcbmBgYCJ9 -->

```r
```r
resultsOutSharp$Rsquared
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDAuODI1OTgyOFxuIn0= -->

```
[1] 0.8259828
```



<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Train multiple seeds model


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->

resultsOutSharp <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSharp$OOB_RMSE



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5yZXN1bHRzT3V0U2hhcnAgPC0gbXVsdGlfY3JmUmVnKGRhdGFJbj1zdGltRGF0YU51bSwgaVZhcnM9aVZhcnMsIGRWYXI9ZFZhciwgc2VlZHM9c2VlZHMsIG50cmVlPW50cmVlLCBtdHJ5PW10cnksIHBlcm1JbXBDb25kVGhyZXM9cGVybUltcENvbmRUaHJlcywgbnBlcm09bnBlcm0sIG1pbnNwbGl0PW1pbnNwbGl0LCBtaW5idWNrZXQ9bWluYnVja2V0KVxuXG4jIHByaW50IG1vZGVsIHByZWRpY3Rpb24gcmVzdWx0c1xucmVzdWx0c091dFNoYXJwJE9PQl9STVNFXG5gYGBcbmBgYCJ9 -->

```r
```r

resultsOutSharp <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSharp$OOB_RMSE
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDAuNjcxMzQ4OFxuXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVjbVZ6ZFd4MGMwOTFkRk5vWVhKd0pFOVBRbDlOUVVWY2JtQmdZQ0o5IC0tPlxuXG5gYGByXG5yZXN1bHRzT3V0U2hhcnAkT09CX01BRVxuYGBgXG5cbjwhLS0gcm5iLXNvdXJjZS1lbmQgLS0+XG4ifQ== -->

[1] 0.6713488

resultsOutSharp$OOB_MAE



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dWNtVnpkV3gwYzA5MWRGTm9ZWEp3SkU5UFFsOU5RVVZjYm1CZ1lGeHVZR0JnSW4wPSAtLT5cblxuYGBgclxuYGBgclxucmVzdWx0c091dFNoYXJwJE9PQl9NQUVcbmBgYFxuYGBgXG5cbjwhLS0gcm5iLXNvdXJjZS1lbmQgLS0+XG4ifQ== -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucmVzdWx0c091dFNoYXJwJE9PQl9NQUVcbmBgYFxuYGBgIn0= -->

```r
```r
resultsOutSharp$OOB_MAE
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDAuNTE4NjI2MVxuXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVjbVZ6ZFd4MGMwOTFkRk5vWVhKd0pGSnpjWFZoY21Wa1hHNWdZR0FpZlE9PSAtLT5cblxuYGBgclxucmVzdWx0c091dFNoYXJwJFJzcXVhcmVkXG5gYGBcblxuPCEtLSBybmItc291cmNlLWVuZCAtLT5cbiJ9 -->

[1] 0.5186261

resultsOutSharp$Rsquared



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dWNtVnpkV3gwYzA5MWRGTm9ZWEp3SkZKemNYVmhjbVZrWEc1Z1lHQmNibUJnWUNKOSAtLT5cblxuYGBgclxuYGBgclxucmVzdWx0c091dFNoYXJwJFJzcXVhcmVkXG5gYGBcbmBgYFxuXG48IS0tIHJuYi1zb3VyY2UtZW5kIC0tPlxuIn0= -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucmVzdWx0c091dFNoYXJwJFJzcXVhcmVkXG5gYGBcbmBgYCJ9 -->

```r
```r
resultsOutSharp$Rsquared
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDAuODM1NTc2XG4ifQ== -->

```
[1] 0.835576
```



<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->




<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->
# store results
resdAnnoyMnFitAB['Abs sharp', 'RMSE'] <- resultsOutSharp$OOB_RMSE
resdAnnoyMnFitAB['Abs sharp', 'MAE'] <- resultsOutSharp$OOB_MAE
resdAnnoyMnFitAB['Abs sharp', 'Rsquared'] <- resultsOutSharp$Rsquared
resdAnnoyMnPermImpAB$AbsSharp <- resultsOutSharp$conditional_permimp



<!-- rnb-output-end -->

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```r
```r
# store results
resdAnnoyMnFitAB['Abs sharp', 'RMSE'] <- resultsOutSharp$OOB_RMSE
resdAnnoyMnFitAB['Abs sharp', 'MAE'] <- resultsOutSharp$OOB_MAE
resdAnnoyMnFitAB['Abs sharp', 'Rsquared'] <- resultsOutSharp$Rsquared
resdAnnoyMnPermImpAB$AbsSharp <- resultsOutSharp$conditional_permimp

```
```

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###### Plot results


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par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutSharp.conimp <- arrange(resultsOutSharp$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutSharp.conimp) + geom_col(aes(x=factor(rownames(resultsOutSharp.conimp), levels=rownames(resultsOutSharp.conimp)), y=CondPermImp), fill=mycolours[2], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (mean change in annoyance)\) + ggtitle(\Sharpness\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar



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```r
```r
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutSharp.conimp <- arrange(resultsOutSharp$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutSharp.conimp) + geom_col(aes(x=factor(rownames(resultsOutSharp.conimp), levels=rownames(resultsOutSharp.conimp)), y=CondPermImp), fill=mycolours[2], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (mean change in annoyance)\) + ggtitle(\Sharpness\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar
```
```

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```

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<img src=\data:image/png;base64
```



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}



<!-- rnb-output-end -->

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```r
```r

if (saveplots){
  ggsave(filename=\PtsABdAnnoyMnSharpConPermimp.svg\, width=8, height=4.9, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdAnnoyMnSharpConPermimp.svg\)
  
  ggsave(filename=\PtsABdAnnoyMnSharpConPermimp.pdf\, width=8, height=4.9, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdAnnoyMnSharpConPermimp.pdf\)
}

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Selected metric


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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sharpVar <- \UASSharpAurISO305ExMaxLR\



<!-- rnb-output-end -->

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```r
```r

sharpVar <- \UASSharpAurISO305ExMaxLR\

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


##### Tonal loudness and tonality

###### Set variables


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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iVars <- c(absVar, eventVar, ambVar, \UASTonalECMAAvgMaxLR\, \UASTonalSHMInt05ExMaxLR\, \UASTonalSHMIntAvgMaxLR\, \UASTonalECMA05ExMaxLR\, \UASTonLdECMAPowAvgBin\, \UASTonLdECMA05ExBin\, \UASTonalAurAvgMaxLR\, \UASTonalAur05ExMaxLR\, \UASTonalAur10ExMaxLR\)
dVar <- \dAnnoyMean\

seeds <- c(540, 104798, 456464, 87331, 94564)



<!-- rnb-output-end -->

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```r
```r

iVars <- c(absVar, eventVar, ambVar, \UASTonalECMAAvgMaxLR\, \UASTonalSHMInt05ExMaxLR\, \UASTonalSHMIntAvgMaxLR\, \UASTonalECMA05ExMaxLR\, \UASTonLdECMAPowAvgBin\, \UASTonLdECMA05ExBin\, \UASTonalAurAvgMaxLR\, \UASTonalAur05ExMaxLR\, \UASTonalAur10ExMaxLR\)
dVar <- \dAnnoyMean\

seeds <- c(540, 104798, 456464, 87331, 94564)

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


###### Hyperparameter tuning


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)



<!-- rnb-output-end -->

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```r
```r

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
```
```

<!-- rnb-source-end -->


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" />

<!-- rnb-plot-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Selected hyperparameters


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVYRzV1ZEhKbFpTQThMU0ExTURGY2JtMTBjbmtnUEMwZ1lYTXVhVzUwWldkbGNpaHNaVzVuZEdnb2FWWmhjbk1wTHpFdU1qVXBYRzVjYm1CZ1lDSjkgLS0+XG5cbmBgYHJcblxubnRyZWUgPC0gNTAxXG5tdHJ5IDwtIGFzLmludGVnZXIobGVuZ3RoKGlWYXJzKS8xLjI1KVxuXG5gYGBcblxuPCEtLSBybmItc291cmNlLWVuZCAtLT5cbiJ9 -->

ntree <- 501
mtry <- as.integer(length(iVars)/1.25)



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dVhHNXVkSEpsWlNBOExTQTFNREZjYm0xMGNua2dQQzBnWVhNdWFXNTBaV2RsY2loc1pXNW5kR2dvYVZaaGNuTXBMekV1TWpVcFhHNWNibUJnWUZ4dVlHQmdJbjA9IC0tPlxuXG5gYGByXG5gYGByXG5cbm50cmVlIDwtIDUwMVxubXRyeSA8LSBhcy5pbnRlZ2VyKGxlbmd0aChpVmFycykvMS4yNSlcblxuYGBgXG5gYGBcblxuPCEtLSBybmItc291cmNlLWVuZCAtLT5cbiJ9 -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5udHJlZSA8LSA1MDFcbm10cnkgPC0gYXMuaW50ZWdlcihsZW5ndGgoaVZhcnMpLzEuMjUpXG5cbmBgYFxuYGBgIn0= -->

```r
```r

ntree <- 501
mtry <- as.integer(length(iVars)/1.25)

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


###### Run model

Train preliminary model


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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# Tonality with tonal loudness

nperm <- 5

resultsOutTonal1 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutTonal1$OOB_RMSE



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


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```r
```r
# Tonality with tonal loudness

nperm <- 5

resultsOutTonal1 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutTonal1$OOB_RMSE
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDAuNjgxMTY2MVxuXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVjbVZ6ZFd4MGMwOTFkRlJ2Ym1Gc01TUlBUMEpmVFVGRlhHNWdZR0FpZlE9PSAtLT5cblxuYGBgclxucmVzdWx0c091dFRvbmFsMSRPT0JfTUFFXG5gYGBcblxuPCEtLSBybmItc291cmNlLWVuZCAtLT5cbiJ9 -->

[1] 0.6811661

resultsOutTonal1$OOB_MAE



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dWNtVnpkV3gwYzA5MWRGUnZibUZzTVNSUFQwSmZUVUZGWEc1Z1lHQmNibUJnWUNKOSAtLT5cblxuYGBgclxuYGBgclxucmVzdWx0c091dFRvbmFsMSRPT0JfTUFFXG5gYGBcbmBgYFxuXG48IS0tIHJuYi1zb3VyY2UtZW5kIC0tPlxuIn0= -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucmVzdWx0c091dFRvbmFsMSRPT0JfTUFFXG5gYGBcbmBgYCJ9 -->

```r
```r
resultsOutTonal1$OOB_MAE
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDAuNTMzMTUxMVxuXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVjbVZ6ZFd4MGMwOTFkRlJ2Ym1Gc01TUlNjM0YxWVhKbFpGeHVZR0JnSW4wPSAtLT5cblxuYGBgclxucmVzdWx0c091dFRvbmFsMSRSc3F1YXJlZFxuYGBgXG5cbjwhLS0gcm5iLXNvdXJjZS1lbmQgLS0+XG4ifQ== -->

[1] 0.5331511

resultsOutTonal1$Rsquared



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dWNtVnpkV3gwYzA5MWRGUnZibUZzTVNSU2MzRjFZWEpsWkZ4dVlHQmdYRzVnWUdBaWZRPT0gLS0+XG5cbmBgYHJcbmBgYHJcbnJlc3VsdHNPdXRUb25hbDEkUnNxdWFyZWRcbmBgYFxuYGBgXG5cbjwhLS0gcm5iLXNvdXJjZS1lbmQgLS0+XG4ifQ== -->


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```r
```r
resultsOutTonal1$Rsquared
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDAuODMyNDcwNVxuIn0= -->

```
[1] 0.8324705
```



<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Train multiple seeds model


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->
# Tonality with tonal loudness

resultsOutTonal1 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutTonal1$OOB_RMSE



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


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```r
```r
# Tonality with tonal loudness

resultsOutTonal1 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutTonal1$OOB_RMSE
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDAuNjcwNDE2OFxuXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVjbVZ6ZFd4MGMwOTFkRlJ2Ym1Gc01TUlBUMEpmVFVGRlhHNWdZR0FpZlE9PSAtLT5cblxuYGBgclxucmVzdWx0c091dFRvbmFsMSRPT0JfTUFFXG5gYGBcblxuPCEtLSBybmItc291cmNlLWVuZCAtLT5cbiJ9 -->

[1] 0.6704168

resultsOutTonal1$OOB_MAE



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dWNtVnpkV3gwYzA5MWRGUnZibUZzTVNSUFQwSmZUVUZGWEc1Z1lHQmNibUJnWUNKOSAtLT5cblxuYGBgclxuYGBgclxucmVzdWx0c091dFRvbmFsMSRPT0JfTUFFXG5gYGBcbmBgYFxuXG48IS0tIHJuYi1zb3VyY2UtZW5kIC0tPlxuIn0= -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucmVzdWx0c091dFRvbmFsMSRPT0JfTUFFXG5gYGBcbmBgYCJ9 -->

```r
```r
resultsOutTonal1$OOB_MAE
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDAuNTIzODAxN1xuXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVjbVZ6ZFd4MGMwOTFkRlJ2Ym1Gc01TUlNjM0YxWVhKbFpGeHVZR0JnSW4wPSAtLT5cblxuYGBgclxucmVzdWx0c091dFRvbmFsMSRSc3F1YXJlZFxuYGBgXG5cbjwhLS0gcm5iLXNvdXJjZS1lbmQgLS0+XG4ifQ== -->

[1] 0.5238017

resultsOutTonal1$Rsquared



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dWNtVnpkV3gwYzA5MWRGUnZibUZzTVNSU2MzRjFZWEpsWkZ4dVlHQmdYRzVnWUdBaWZRPT0gLS0+XG5cbmBgYHJcbmBgYHJcbnJlc3VsdHNPdXRUb25hbDEkUnNxdWFyZWRcbmBgYFxuYGBgXG5cbjwhLS0gcm5iLXNvdXJjZS1lbmQgLS0+XG4ifQ== -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucmVzdWx0c091dFRvbmFsMSRSc3F1YXJlZFxuYGBgXG5gYGAifQ== -->

```r
```r
resultsOutTonal1$Rsquared
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDAuODM3OTk2OVxuIn0= -->

```
[1] 0.8379969
```



<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->



<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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# store results
resdAnnoyMnFitAB['Abs tonal inc loud', 'RMSE'] <- resultsOutTonal1$OOB_RMSE
resdAnnoyMnFitAB['Abs tonal inc loud', 'MAE'] <- resultsOutTonal1$OOB_MAE
resdAnnoyMnFitAB['Abs tonal inc loud', 'Rsquared'] <- resultsOutTonal1$Rsquared
resdAnnoyMnPermImpAB$AbsTonal1 <- resultsOutTonal1$conditional_permimp



<!-- rnb-output-end -->

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```r
```r
# store results
resdAnnoyMnFitAB['Abs tonal inc loud', 'RMSE'] <- resultsOutTonal1$OOB_RMSE
resdAnnoyMnFitAB['Abs tonal inc loud', 'MAE'] <- resultsOutTonal1$OOB_MAE
resdAnnoyMnFitAB['Abs tonal inc loud', 'Rsquared'] <- resultsOutTonal1$Rsquared
resdAnnoyMnPermImpAB$AbsTonal1 <- resultsOutTonal1$conditional_permimp

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


###### Plot results


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutTonal1.conimp <- arrange(resultsOutTonal1$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutTonal1.conimp) + geom_col(aes(x=factor(rownames(resultsOutTonal1.conimp), levels=rownames(resultsOutTonal1.conimp)), y=CondPermImp), fill=mycolours[3], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (mean change in annoyance)\) + ggtitle(\Tonality inc. tonal loudness\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 1.4))
pBar



<!-- rnb-output-end -->

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```r
```r

par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutTonal1.conimp <- arrange(resultsOutTonal1$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutTonal1.conimp) + geom_col(aes(x=factor(rownames(resultsOutTonal1.conimp), levels=rownames(resultsOutTonal1.conimp)), y=CondPermImp), fill=mycolours[3], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (mean change in annoyance)\) + ggtitle(\Tonality inc. tonal loudness\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 1.4))
pBar
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

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```

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<img src=\data:image/png;base64
```



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" />

<!-- rnb-plot-end -->


<!-- rnb-output-end -->

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if (saveplots){
  ggsave(filename=\PtsABdAnnoyMnTonalLdConPermimp.svg\, width=8, height=3.8, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdAnnoyMnTonalLdConPermimp.svg\)
  
  ggsave(filename=\PtsABdAnnoyMnTonalLdConPermimp.pdf\, width=8, height=3.8, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdAnnoyMnTonalLdConPermimp.pdf\)
}



<!-- rnb-output-end -->

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```r
```r

if (saveplots){
  ggsave(filename=\PtsABdAnnoyMnTonalLdConPermimp.svg\, width=8, height=3.8, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdAnnoyMnTonalLdConPermimp.svg\)
  
  ggsave(filename=\PtsABdAnnoyMnTonalLdConPermimp.pdf\, width=8, height=3.8, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdAnnoyMnTonalLdConPermimp.pdf\)
}

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Selected metric


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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tonLdVar <- \UASTonLdECMAPowAvgBin\



<!-- rnb-output-end -->

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```r
```r

tonLdVar <- \UASTonLdECMAPowAvgBin\

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


##### Tonality without tonal loudness

###### Set variables


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->

iVars <- c(absVar, eventVar, ambVar, \UASTonalECMAAvgMaxLR\, \UASTonalSHMInt05ExMaxLR\, \UASTonalSHMIntAvgMaxLR\, \UASTonalECMA05ExMaxLR\, \UASTonalAurAvgMaxLR\, \UASTonalAur05ExMaxLR\, \UASTonalAur10ExMaxLR\)
dVar <- \dAnnoyMean\

seeds <- c(156089, 5860, 10528, 89541, 4685146)



<!-- rnb-output-end -->

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```r
```r

iVars <- c(absVar, eventVar, ambVar, \UASTonalECMAAvgMaxLR\, \UASTonalSHMInt05ExMaxLR\, \UASTonalSHMIntAvgMaxLR\, \UASTonalECMA05ExMaxLR\, \UASTonalAurAvgMaxLR\, \UASTonalAur05ExMaxLR\, \UASTonalAur10ExMaxLR\)
dVar <- \dAnnoyMean\

seeds <- c(156089, 5860, 10528, 89541, 4685146)

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


###### Hyperparameter tuning


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)



<!-- rnb-output-end -->

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```r
```r

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
```
```

<!-- rnb-source-end -->


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jJgwV5BHTI+17Fbl9u3bX3jljfz8RMvemZmZ5u39/X1vTwF05fHdFeRPmd70lZifPf9+IdQ85mMEzmPAiIlhCvGI6a2W3XthYWPj+tbduez91uTc3t52b8zMzIyPj4vI//3Q0/MAHbnvrsGNhvQ6Q/0l5uGPhtU/hJPHfHwc+jUGfY+Y8tPhdBDhFuIR01uVPTExISJXr79x7eF7e3o6BJybw7GRAbZh97oPWhOzdVEBCwzgn8eHFwbYht3rs2DEhMmCPGJq+Ry89967cmVeRyTgHKkAf8rUQVtiUsLiHDw+DPu8bB0jJowU5BHT09+OvfvZV9/8QOQLr7yR55BMGCfImekFiQkThb6WJTFhtCCPmJ7+dkzM5zf4cAljqZHgZqYXJCZMFPr1siQmjBbkEdOExX+APw4vWANsJ+M4+YRlWYm8c2Lf0d5ObQAceXIwNsA27F4DUaFrxPQDtSyiS40MsrVz8ovVFaXUSnXxqFJ1NqsLNaVUbWHtTqlLGwAtnqjRAbZh9xqICj0jpj+oZRFd767fcDc1ap2+NVu+u36jLYizuTadTopIMj29tnlUqO5UayJSq06nk13bAGh6fDA2wHYyTj/fk3RpBqCrngNlx+18+sYXNIiu+MK/rN/qlW9HLUUqa//w+IPTU+5R/rGp6d3GPjtbTmcsK7VUVAURp2MbAC2eajn2q/4dSLKUSeSdctZ2d25WF2qqbDv5xJ1StpAUJ59YlHtK2RqeEYiGwwAf+8W8LKJLjQ2yneDOwIrUqjtHO538blqpoqQypa5tABz5jy/9W3d7cjh6+tbasi1IP9+TiJTuLFcqyzHLctMTQG+aRkxfnF7LljJ8A4PwUiPWAFtbEHt2YWe9JCKl9Z2F2cY0T+nOmsREkoWirJe6tBkciYkQ+rV3v+puTw9HTt9aW54I0/IdSHOfnS2n1y3LWk8XkiJSWpeiUkrVcju3dCYSiYkw0zJiSvflPcf2lzKWZfX/afP0WjZZUPdk8UwBAWMcjg2ytbOzK5KyLOvW1L2sLU4+kSmJJG8srMUsy7JSkk62t/GMxEQIPTkcG2A7Eaaf70nq7NmFadGIxESY6RkxuxwJ3Vj2o9yFQSVJK6WUKiT761vPNQZ2tqyUUiq93r5yHjCcrqMykwXVzEA7Wy4k5ShxGqnY2kYHEhNh8/RgZICtLUg/35NIbMptI7WqTOpdMktiIrS++7tfd7eeo2Sz5Xd/9+ttQbqsAmpb9lNaT6XOlD19rZd18gkrtRrP1VT9Qyf5iTA4HLUG2Ibd6yMkJsLk6eHoAFt7lP6+J7k3dcs6WnOgGYmJUPrib911t56jZLPlF3/r7okwnVYBtS37cWI3lFIr1VjfqdNzvaxlWVbLxK/7ofOe3CE3YTw1OsgWACQmQujZwegA28k4/XxPUr+vuZIlMRFmmkbM046Eri/7sW1bRJKF2kK1z5NY9pqXXSp2+mK0Vl2tdwYwl7G1LImJEHp2ODLANuxeH0diIry0jJidVwF1XvbjbK71uwio1x+C1fVOK9iTBd2fZ4Hzdzg6yBYIJCZCR8t62SEjMRFeekbMjquAji/7aVzaZFHu6Tn2K5leOnbfyec5OBPhMTLQNnwkJkLo8HBkgG3YvW5FYiLUNI2YnVYBHVv201gSdIaDpXusl11fXU1ZR2LL1bP83kCgGTsvS2IihJ4+Gx1gG3avW5GYCLMgj5i95mVzuZo6UstNnVO/AP/pOifXuSMxEUKHyhpgG3avW5GYCLMgj5inX1+stCuzR4sVnPxidbLsd4+A8xKUA7nOjMRECB0eBKowHQCJiTAL8ojZrZZ18onYckVEZHm5ZXc850hygFNL/6Pa/Nl/qIeOZ2Px7tLYU+0xRyylPaaIfPT0oh9hL4490x7zwJ9lbV/xdprzYEyynonOxMz+4K/r69iRT4091h4zdvlPtMf85Ij+fv7ps+e0xxwf+7n2mCLys4PL2mNmvP24Ctbi1zPRPGL+85r+Q8We+DNifvLiE+0xR/0ZMT98csGPsGMj+nt7cOjL57qv/oqnHw/yiNmta+7S2+LxL0x0XrYIGDo1Nsg2VCQmQuvwmTXANuxeu0hMhF+QR8we62WzxzORozIRJkFe/XMqEhMhpA6tAbZh97oViYkwC/KI2eN56pcx4ahMhJG510ogMRFCh9YgW5CQmAixII+YPY79WpeiUpzjGeEUjEnWAZCYCCFl/rFfJCZCLMgjZo81Bjemdlu/IuEbE4SJrk+Z7kVKEq1XXG9ct8SyrIybM/UZm4yODCIxEULWgTXANuxetyIxEWZBnpftUWbXqsuc+RlhpWf1j5NfrK4opVaqi81q1qlN3nMP/ijmpmIiIiVJq+ZVTTwjMRFC5q8xIDERYkFeL9trjcFOrqY4EhPh5Lx23b3xF15//fSW3/v617sG2VybTpdFJJmevrXpuAd/2Ml6ydo44WRpPZVKxXM1Pcc1k5gIoYBNsg6AxESYqTFfzpWmRY81BoWVyVrLfb4xQZj8cu51d+v5ybLZ8pdzJ6veaXfmVWJT0+0PlXZl1hYRJ3ZDKbVSjR1biDAwEhNhdDjQFiAkJsIsyPOypz9PKZNK8Y0JwkqNqgG2E2F2qu7wVavutD3SKGXFtm0RSRZqC9VNDcUsiYkQMn+9LImJMNM0Yvqix7wsV5dGmFkDbcfZsws76yURKa3vLMwe+3axWco2OJtrMqnhC0gSEyFkHQ6yBQmJiVDTMWL6pMc1GZLZbOtdO8tKIISHntU/dnZFLMuSeK5WtsXJJ+5MlgtJab04e+MCl/Fcrazl2C8SE+FjBeUiXoMjMRFi5q6XBUJtRA2ynZAsHF2u0s6WG6cqOLoIkHuBS65oCZzC/HlZINQ0jZgdzmJ5Yn+3Nl275vFXA8wV5NU/QNRYB4NsAM6HnhGz01ksRcTJJxblXn3Kp0ubU1DLIrqsUTXANuxeA+Gka16204xO4+IlrXudfELLpUuAaPj+b77mbj1HyWbL7//ma21BnM216XRSRJLp6bWjQ6FLd5YrleWYezmhLm1O02O9LBBmnb7+ADAUeiZZ6zM6yVImkXcaq3qczepCTZVtJ5+4U8q6y9kzseXKUlHHUwKR8OKb/6J+q9cs6FFLkQev/OPjD7acxXK3sa9x+Wcnn1jMO/c6tjkV87KILmtEDbANu9dAOP2f377ubj3nYltbtuk2o+OeOa9WdR+UUmY9XcvFz+13A8ynacTsfhZLEXt2YbpXm46Yl0V0jQT4qEwgaqZu3nVv9DydwaktO83o2NlyOmNZqaWiKohbyRYKkr/luc9AdGgZMe3ZhZ07JUkmS+s7CzcaR0PHptydUqvKZNaOdWpzKmpZRJc1wlHQQFBoOpBrp1oTsd0ZndnGTie/m1YqnbEyJXVj99bqamV11X1SKaqClhPlASGnZ8TsfBbL7L2phGWlpP5x81ibfuhYY7B3P3t3S0Mc4HyNjKoBtmH3um8kJoyi5TwGna9dUrqzJjGRZKEo66XGKfJqufjSMApZEhNm0jVidjyLZT0tm6e0LJztNJbea9mtu6+++YHnKMD5G7HUANuwe90nEhOG0XMeAzu7IinLsm5N3cvajXMVJG8srMUsy7JSkh7yJCyJCVMFecT0usZg624l/sYrD9aO7ZyZmWne3t/f9/gUQDce311jY6E9OyWJifPn8U01oikdkwWlCvXbdrZcqP+rsm3tGo+dJxITQxTiEdNbLbt1txK/fl3uv3V89/b2tntjZmZmfHzc01MA3Xl8d42Gdb1sn4n5x+ffM4SZ17/2IU3HI4yYGKoQj5heatm9+2+9884H77wjIiJzcnPj+lU9nQLOxYXR4GamByQmjKRrXjaoSEyYLcgjppdadmI+vzEvInv3s2tX8qQlTDMW4E+ZHpCYMFLYL0hLYsJsQR4xOScXomtsNOSDJ2CQsNeygNmCPGLqOCfXxDwfMWGiCyMHA2wn43S4/nvj8u+We3npzteI9xmJCaOMHKgBtmH3+uxITJhJ14jpB65hi+i6MHI4wNYepX79d7VSXWxWqk5t8p5SSilVzE3FOrcB0ErPObkA+EPPiOkP1hggur7zN+tXwvy1d796ess/vPavuj3kbK5Np8sikkxP39p0su75n5P1k1iWdmU2KU6+QxsArUaeDbsHALq7OBrcFKWWRXT9evnvuzcu9Pp+otlSRL6Z+DfHH+x0/fc6t5QV57Q2AERErEMDFwwAkXFuk6wDoJZFdF3Us5Sn4/XfRaRZyp7aBoCIiFjBnfQBoGvE9AXrZRFdF0aeDbC1Bel8/XcRcUtZu0cbAK6oHPsFmEnLiOkT5mURXZe0rP6xsytiWZbEc7WyLU4+cWeyXEhK66xsWxsAJ1kUpkCA6Rkx/UEti+i6qOmElp2u/y4iyWy2cxsAJ3FSAiDIdI2YfmCNAaLr4uizAbZh9xoIp5GnaoBt2L0GokLXiNn5bOulTMsJ2U/c7YV5WUTXJQ42AQKD8xgAQaZnxKyfbT1ZyiTyTrl5gsqSpFu/u2y72xO1LKLrIie0BAKD9bJAkH3jr77t3lj5379+estbf+Wb3R7qeEZ2kdJ6KpWK52r14rbtbm/Usoiuy8zLAoFBLQsE2a2dL7s3Llv9thSRlen/dPzBTmdbd2I3lCqUMlYiXytn7ba7ffSNWhbRdWn06bC7AKBu5BkHfwHBpWnE7HS2ddu2RSRZqO1mNh3Jtt/tHZRaFtHFelkgQJiXBQJMy4hpzy7s3ClJMlla31m40ValOptrMpntdvc01LKIrksW87JAUFjMywIBpmfE7HRG9hu7idhyRSSeq5WTIk7+2N1+cE4uRNelkacDbMPuNRBO1sHBANvJOJ3O+OPua+6tn++n/axAALrTNWImC0op5a6DtbPlQlLsbFkd7Wu/249zmpf9+R88rz3m009qDyki8pNP+RLWD6NPfAl76MObQo3qjyki8quefjrix379l2/O+BH2owkfzqf9Sf0xrVH9s4DqZxe0x5TLvpyf3Lqg/9fP/EVPP65nXrbjGX+czepCTZVtJ5+4U8re2N1NK1WQUsa6U8oW+pz4OU8f/+Gf0x7zyWe0hxQR+fCy/pg+nZLfp/PWKB+mBP2IKSLi7b0e5BGTNQaILtYYAMHxrf/1z9wbX5q+2aPlzu1uLbuc8ad+vEmtOp3Oip109ybTS7d2HUlyWWmgD0EeMallEV2XWTAABEbyL/0T90avE/6c3rLTGX/sbDmdsazUUvH46denFmYpZIH+BHnEpJZFdAX5UyYQOc+0fLXc6Yw/4uR300qlM1ampBqLCkqZ9cnCGa4sBERbkEdMallEV5AzE4icZ76d8ad0Z01uZMUuFNczJUkmRUoZaz19lmtkAlEX5BGT8xggui5bBwNsJ+N0Om76xP76kdOZ0jn8YoCJDg4H2drY2RVJWZZ1a+pe1hYnn8iURJI3FtZilmVZKUknxcknrNSqrKZISKB/ukZMPzAvi+jSc62EjsdNizj5xKLcU6p+vyRpxSwQcAod87LinvGnkWp2tlyo/6uOzrl+7A6AvgT56kLUsoiu6St77g3n0edPb2m/8INuD3U5brp0Z7lSkZi1vFRUhaSU1lOpVDzX56WlgSjSs14WgC8uW8G9Mh+1LKLr0V79tMeXex043WwpIi9M/PD4g52Omy6tS1GppDj5xGLeSc7GbihVKGWsRJ5yFuhIaZqXBeAHalkgiC5ZWtaLdzxuus6eXZjeFLFtW0SShdpuZtMRilmgA2pZIMA0jZi+CG7PAL9dktEBtrYg9uzCznpJRErrO0cnq4xNuTulVpXJZunqbK613APQQj19NsA27F4DUaFlxPQJ87KIrouWjjSzsytiWZbEc7WyLU4+cWeyXEhm700lLCslS0VVECefiC1XROK5WjmAF8wEgkAdUJgCwaVnxPQHtSyi6/LzH2iJ0+m46WMHTtscNw308p0nvz/sLgDoSteI6QfWGAAAAMBU1LIAAAAwFbUsAAAATEUtCwAAAFNRywIAAMBU1LIAAAAwFbUsAAAATOWtlnwLZV4AAAh1SURBVN27n52bm5uby97f09QfAJ6RmEAAkZiAPzzOy7702sbGxsbNF999j9QEAoPEBAKIxAR84e26XxMTEyKy9/DKV+YnWnbPzMw0b+/v73t6CqA73l2dkZgYBt5UPZCYGKoQv7u8XsN26+7c7XdEXr4yf/Vqc+f29rZ7Y2ZmZnx83ONTAN3w7uqGxMT5403VE4mJIQrxu8vrsV9Xr29sbLzxyoPKlpbuANCBxAQCiMQE/KDlPAYTV65deUFHIAD6kJhAAJGYgGae1hjUvy4RefnmxnU9/QHgFYkJBBCJCfjEUy179foGGQkEDYkJBBCJCfiEayUAAADAVNSyAAAAMBW1LAAAAExFLQsAAABTUcsCAADAVNSyAAAAMBW1LAAAAExFLQsAAABTUcsCAADAVNSyAAAAMBW1LAAAAExFLQsAAABTUcsCAADAVNSyAAAAMBW1LAAAAEw1dj5P87l/9z+0x7QuXNAeU0QOPvzQj7B+GPHnFTh8/Fh/UMufT03/1JeoEfHif3joS9xffKQ95LM/3dcec/S557THtC5f0h5THRxoj+mXvzvsDoTF5/79/9Qf1LL0xxRRT574ENWXrlpjvhQ8hx/r/4vn0ysgv33dl7ABwLwsAAAATEUtCwAAAFNRywIAAMBU1LIAAAAwFbUsAAAATEUtCwAAAFNRywIAAMBU1LIAAAAwFbUsAAAATEUtCwAAAFNRywIAAMBU1LIAAAAwFbUsAAAATEUtCwAAAFNRywIAAMBU1LIAAAAwFbUsAAAATEUtCwAAAFNRywIAAMBUHmvZrbtzc3Nzc9n7e3q6A0ADEhMIIBIT8IWnWnbv/sP4xsbGxs0X31zb0tUjAN6QmEAAkZiAT8a8/PDE/PyEiMjV+MtvPdyTqxP1/TMzM802+/v7Xp4COAXvro5ITAwFb6rTkZgYrhC/uzzVskeuXHtp4uje9va2e2NmZmZ8fFzPUwAn8O7qgcTEOeJN1S8SE8MQ4neXjmO/tu5WrsxP9G4H4ByRmEAAkZiAbp5r2a27c5X49as6+gJAFxITCCASE/CBx2O/snO335F3bs/Nzd1lKTsQDCQmEEAkJuATj8d+5TfmdfUEgB4kJhBAJCbgE66VAAAAAFNRywIAAMBU1LIAAAAwFbUsAAAATEUtCwAAAFNRywIAAMBU1LIAAAAwFbUsAAAATEUtCwAAAFNRywIAAMBU1LIAAAAwFbUsAAAATEUtCwAAAFNRywIAAMBU1LIAAAAw1dj5PM23f/F7fbbc398fHx/X3gE/wtJVs8KiTemD1/tvbMrbMsoxfQpLPp6/b3/4Zp8teSNFvKv+hTUL87IAAAAwFbUsAAAATEUtCwAAAFNRywIAAMBUvh/7NTMz4/dTADgrEhMIIBITGIQKmC9+8YumhKWrZoWFF6a8LaMc06ew5GOQ8UaKeFf9C2sW1hgAAADAVJZSath9AAAAAAbBvCwAAABMRS0LAAAAU1HLAgAAwFRDrGW37s7Nzc3NZe/vHd9xd0tEZO9+tnl7sMD1n3UDNZ7lrGHb2x+PNnDwEy019Pn019Pb67B3P9toq+kVaO+e1v81eOFPYgY9KyOfkuRj0DFiRjg9T3SPDG03rBMoPHr77feVUur917/8+vtKKaXef//99iZfe/39Ez/Y07E4jRjvv/61tx8NFralfVs0T8GPtfTe5x6vp6euvv/6l79cj6rvFWj779b9v4YB+ZSYJmRlpFOSfAw4Rswop2d798jQE4Y2LzsxP39VRORq/OUHD/dEZKty+3bLR6SBHYuz9967L8avisjV+Ivvvuc1dls0fcE19Pn019NLV7fuVuJvvPKFTnE8hG377/bxfw1n4k9iGpeVUUtJ8jHoGDH19tmo9GzvHhl6UgDWy1659tKEyN4LCxsbG195+KrH5GyP8+KVF0RE5IUrL3rvans0TcH19rnz6zlozK27lfj1q0f3Nb0Cbd3z938NA9GYmMZlZdRSknw0CCNm1NJTyNDehl3Lbt2tXJmfEJGJiQkRuXr9jWsPPX2uaI/z4OEjERF59PCB586eiKYpuMY+d309B4u5d/+td965PTf36psfvHN77u6WtlegrXv+/q/h7PQmpnFZGbWUJB9NwYgp0UvPk90jQ08Yai27dXfu+EcYkb333pUrE95D1+NMvHTtQWVLRLYqD6695DVuWzS9wTX0ufvrOWjMifn8xsbGxsYbr3zh5Zsb16/qfgXa/rt9+V/DmfmVmMZlZdRSknwMNkZMvX02LD2FDD3NsBbqPnr7a19ueP395t3mymV3+XTrjjOFbY/TvH/WsG3tO0YbIHhrSy197vP1HPB1aFlHruUVaOue9v81DMyPxDQiK6OckuRj8DFiRjY9FRnaB65hCwAAAFMNe70sAAAAMChqWQAAAJiKWhYAAACmopYFAACAqahlh8/JZ/LOsDsBoIGUBAKL9MRJ1LLDV6vuDLsLAI6QkkBgkZ44iVr2nJUyVqZUyliWZSXyjoiUMqnVynLMypTqj+bzCfcxt1W9mbTfBaADKQkEFumJ/gz7BLeRUsvFRURkqahUcUniuZqq1Yq5eDxXO3rUvaOKS+6Neru2uwA0ICWBwCI90a+xIZTP0WVny8WqtZ4uJEWcqbiIiG3H2h/N2iLi7O5UlmPWsoiILNUcOXZXxB5C94GwISWBwCI90S/WGATXUrHxiaOQPHkXwDkjJYHAIj2jjFo2oOzJ6dX1koiIlDKZUtvdYfYMiCRSEggs0jPq/F3CgOOKSyIislRsXenj7lwqNh5trO6p3218vGy7C8A7UhIILNITfbKUUjpLYwAAAOC8sMYAAAAApqKWBQAAgKmoZQEAAGAqalkAAACYiloWAAAApqKWBQAAgKmoZQEAAGCq/w/pwHwLGrED/wAAAABJRU5ErkJggg==\" 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" />

<!-- rnb-plot-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Selected hyperparameters


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVYRzV1ZEhKbFpTQThMU0F4TURBeFhHNXRkSEo1SUR3dElHRnpMbWx1ZEdWblpYSW9iR1Z1WjNSb0tHbFdZWEp6S1M4eExqSTFLVnh1WEc1Z1lHQWlmUT09IC0tPlxuXG5gYGByXG5cbm50cmVlIDwtIDEwMDFcbm10cnkgPC0gYXMuaW50ZWdlcihsZW5ndGgoaVZhcnMpLzEuMjUpXG5cbmBgYFxuXG48IS0tIHJuYi1zb3VyY2UtZW5kIC0tPlxuIn0= -->

ntree <- 1001
mtry <- as.integer(length(iVars)/1.25)



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dVhHNXVkSEpsWlNBOExTQXhNREF4WEc1dGRISjVJRHd0SUdGekxtbHVkR1ZuWlhJb2JHVnVaM1JvS0dsV1lYSnpLUzh4TGpJMUtWeHVYRzVnWUdCY2JtQmdZQ0o5IC0tPlxuXG5gYGByXG5gYGByXG5cbm50cmVlIDwtIDEwMDFcbm10cnkgPC0gYXMuaW50ZWdlcihsZW5ndGgoaVZhcnMpLzEuMjUpXG5cbmBgYFxuYGBgXG5cbjwhLS0gcm5iLXNvdXJjZS1lbmQgLS0+XG4ifQ== -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5udHJlZSA8LSAxMDAxXG5tdHJ5IDwtIGFzLmludGVnZXIobGVuZ3RoKGlWYXJzKS8xLjI1KVxuXG5gYGBcbmBgYCJ9 -->

```r
```r

ntree <- 1001
mtry <- as.integer(length(iVars)/1.25)

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


###### Run model

Train preliminary model


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->
# Tonality

nperm <- 5

resultsOutTonal2 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutTonal2$OOB_RMSE



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuIyBUb25hbGl0eVxuXG5ucGVybSA8LSA1XG5cbnJlc3VsdHNPdXRUb25hbDIgPC0gY3JmUmVnKGRhdGFJbj1zdGltRGF0YU51bSwgaVZhcnM9aVZhcnMsIGRWYXI9ZFZhciwgc2VlZHM9c2VlZHNbMToyXSwgbnRyZWU9bnRyZWUsIG10cnk9bXRyeSwgcGVybUltcENvbmRUaHJlcz1wZXJtSW1wQ29uZFRocmVzLCBucGVybT1ucGVybSwgbWluc3BsaXQ9bWluc3BsaXQsIG1pbmJ1Y2tldD1taW5idWNrZXQpXG5cbiMgcHJpbnQgbW9kZWwgcHJlZGljdGlvbiByZXN1bHRzXG5yZXN1bHRzT3V0VG9uYWwyJE9PQl9STVNFXG5gYGBcbmBgYCJ9 -->

```r
```r
# Tonality

nperm <- 5

resultsOutTonal2 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutTonal2$OOB_RMSE
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDAuNjc2NzU5NlxuXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVjbVZ6ZFd4MGMwOTFkRlJ2Ym1Gc01pUlBUMEpmVFVGRlhHNWdZR0FpZlE9PSAtLT5cblxuYGBgclxucmVzdWx0c091dFRvbmFsMiRPT0JfTUFFXG5gYGBcblxuPCEtLSBybmItc291cmNlLWVuZCAtLT5cbiJ9 -->

[1] 0.6767596

resultsOutTonal2$OOB_MAE



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dWNtVnpkV3gwYzA5MWRGUnZibUZzTWlSUFQwSmZUVUZGWEc1Z1lHQmNibUJnWUNKOSAtLT5cblxuYGBgclxuYGBgclxucmVzdWx0c091dFRvbmFsMiRPT0JfTUFFXG5gYGBcbmBgYFxuXG48IS0tIHJuYi1zb3VyY2UtZW5kIC0tPlxuIn0= -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucmVzdWx0c091dFRvbmFsMiRPT0JfTUFFXG5gYGBcbmBgYCJ9 -->

```r
```r
resultsOutTonal2$OOB_MAE
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDAuNTI0MzQ1MVxuXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVjbVZ6ZFd4MGMwOTFkRlJ2Ym1Gc01pUlNjM0YxWVhKbFpGeHVZR0JnSW4wPSAtLT5cblxuYGBgclxucmVzdWx0c091dFRvbmFsMiRSc3F1YXJlZFxuYGBgXG5cbjwhLS0gcm5iLXNvdXJjZS1lbmQgLS0+XG4ifQ== -->

[1] 0.5243451

resultsOutTonal2$Rsquared



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dWNtVnpkV3gwYzA5MWRGUnZibUZzTWlSU2MzRjFZWEpsWkZ4dVlHQmdYRzVnWUdBaWZRPT0gLS0+XG5cbmBgYHJcbmBgYHJcbnJlc3VsdHNPdXRUb25hbDIkUnNxdWFyZWRcbmBgYFxuYGBgXG5cbjwhLS0gcm5iLXNvdXJjZS1lbmQgLS0+XG4ifQ== -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucmVzdWx0c091dFRvbmFsMiRSc3F1YXJlZFxuYGBgXG5gYGAifQ== -->

```r
```r
resultsOutTonal2$Rsquared
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDAuODMxMzI1M1xuIn0= -->

```
[1] 0.8313253
```



<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Train multiple seeds model


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->
# Tonality

resultsOutTonal2 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutTonal2$OOB_RMSE



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


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```r
```r
# Tonality

resultsOutTonal2 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutTonal2$OOB_RMSE
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDAuNjc5NzEzNVxuXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVjbVZ6ZFd4MGMwOTFkRlJ2Ym1Gc01pUlBUMEpmVFVGRlhHNWdZR0FpZlE9PSAtLT5cblxuYGBgclxucmVzdWx0c091dFRvbmFsMiRPT0JfTUFFXG5gYGBcblxuPCEtLSBybmItc291cmNlLWVuZCAtLT5cbiJ9 -->

[1] 0.6797135

resultsOutTonal2$OOB_MAE



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dWNtVnpkV3gwYzA5MWRGUnZibUZzTWlSUFQwSmZUVUZGWEc1Z1lHQmNibUJnWUNKOSAtLT5cblxuYGBgclxuYGBgclxucmVzdWx0c091dFRvbmFsMiRPT0JfTUFFXG5gYGBcbmBgYFxuXG48IS0tIHJuYi1zb3VyY2UtZW5kIC0tPlxuIn0= -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucmVzdWx0c091dFRvbmFsMiRPT0JfTUFFXG5gYGBcbmBgYCJ9 -->

```r
```r
resultsOutTonal2$OOB_MAE
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDAuNTI3MDA3XG5cbjwhLS0gcm5iLXNvdXJjZS1iZWdpbiBleUprWVhSaElqb2lZR0JnY2x4dWNtVnpkV3gwYzA5MWRGUnZibUZzTWlSU2MzRjFZWEpsWkZ4dVlHQmdJbjA9IC0tPlxuXG5gYGByXG5yZXN1bHRzT3V0VG9uYWwyJFJzcXVhcmVkXG5gYGBcblxuPCEtLSBybmItc291cmNlLWVuZCAtLT5cbiJ9 -->

[1] 0.527007

resultsOutTonal2$Rsquared



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dWNtVnpkV3gwYzA5MWRGUnZibUZzTWlSU2MzRjFZWEpsWkZ4dVlHQmdYRzVnWUdBaWZRPT0gLS0+XG5cbmBgYHJcbmBgYHJcbnJlc3VsdHNPdXRUb25hbDIkUnNxdWFyZWRcbmBgYFxuYGBgXG5cbjwhLS0gcm5iLXNvdXJjZS1lbmQgLS0+XG4ifQ== -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucmVzdWx0c091dFRvbmFsMiRSc3F1YXJlZFxuYGBgXG5gYGAifQ== -->

```r
```r
resultsOutTonal2$Rsquared
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDAuODMwMzMxOFxuIn0= -->

```
[1] 0.8303318
```



<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->




<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->

# store results
resdAnnoyMnFitAB['Abs tonal no loud', 'RMSE'] <- resultsOutTonal2$OOB_RMSE
resdAnnoyMnFitAB['Abs tonal no loud', 'MAE'] <- resultsOutTonal2$OOB_MAE
resdAnnoyMnFitAB['Abs tonal no loud', 'Rsquared'] <- resultsOutTonal2$Rsquared
resdAnnoyMnPermImpAB$AbsTonal2 <- resultsOutTonal2$conditional_permimp



<!-- rnb-output-end -->

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```r
```r

# store results
resdAnnoyMnFitAB['Abs tonal no loud', 'RMSE'] <- resultsOutTonal2$OOB_RMSE
resdAnnoyMnFitAB['Abs tonal no loud', 'MAE'] <- resultsOutTonal2$OOB_MAE
resdAnnoyMnFitAB['Abs tonal no loud', 'Rsquared'] <- resultsOutTonal2$Rsquared
resdAnnoyMnPermImpAB$AbsTonal2 <- resultsOutTonal2$conditional_permimp

```
```

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###### Plot results


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<!-- rnb-chunk-begin -->


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par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutTonal2.conimp <- arrange(resultsOutTonal2$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutTonal2.conimp) + geom_col(aes(x=factor(rownames(resultsOutTonal2.conimp), levels=rownames(resultsOutTonal2.conimp)), y=CondPermImp), fill=mycolours[3], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (mean change in annoyance)\) + ggtitle(\Tonality w/o tonal loudness\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 1.4))
pBar



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```r
```r
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutTonal2.conimp <- arrange(resultsOutTonal2$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutTonal2.conimp) + geom_col(aes(x=factor(rownames(resultsOutTonal2.conimp), levels=rownames(resultsOutTonal2.conimp)), y=CondPermImp), fill=mycolours[3], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (mean change in annoyance)\) + ggtitle(\Tonality w/o tonal loudness\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 1.4))
pBar
```
```

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```

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<img src=\data:image/png;base64
```



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/>

<!-- rnb-plot-end -->


<!-- rnb-output-end -->

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if (saveplots){
  ggsave(filename=\PtsABdAnnoyMnTonalConPermimp.svg\, width=8, height=3.2, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdAnnoyMnTonalConPermimp.svg\)
  
  ggsave(filename=\PtsABdAnnoyMnTonalConPermimp.pdf\, width=8, height=3.2, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdAnnoyMnTonalConPermimp.pdf\)
}



<!-- rnb-output-end -->

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```r
```r

if (saveplots){
  ggsave(filename=\PtsABdAnnoyMnTonalConPermimp.svg\, width=8, height=3.2, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdAnnoyMnTonalConPermimp.svg\)
  
  ggsave(filename=\PtsABdAnnoyMnTonalConPermimp.pdf\, width=8, height=3.2, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdAnnoyMnTonalConPermimp.pdf\)
}

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Selected metric


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tonalVar <- \UASTonalSHMInt05ExMaxLR\



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```r
```r

tonalVar <- \UASTonalSHMInt05ExMaxLR\

```
```

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<!-- rnb-output-end -->

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##### Fluctuation strength

###### Set variables


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# Fluctuation strength
iVars <- c(absVar, eventVar, ambVar, \UASFluctSHM10ExBin\, \UASFluctSHM05ExBin\, \UASFluctFZ10ExMaxLR\, \UASFluctFZ05ExMaxLR\, \UASFluctOV10ExMaxLR\, \UASFluctOV05ExMaxLR\)
dVar <- \dAnnoyMean\

seeds <- c(25107, 546098, 195, 5937, 102658)



<!-- rnb-output-end -->

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```r
```r

# Fluctuation strength
iVars <- c(absVar, eventVar, ambVar, \UASFluctSHM10ExBin\, \UASFluctSHM05ExBin\, \UASFluctFZ10ExMaxLR\, \UASFluctFZ05ExMaxLR\, \UASFluctOV10ExMaxLR\, \UASFluctOV05ExMaxLR\)
dVar <- \dAnnoyMean\

seeds <- c(25107, 546098, 195, 5937, 102658)

```
```

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###### Hyperparameter tuning


<!-- rnb-text-end -->


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p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)



<!-- rnb-output-end -->

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```r
```r

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
```
```

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<!-- rnb-output-end -->

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\" 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" />

<!-- rnb-plot-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Selected hyperparameters


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVYRzV1ZEhKbFpTQThMU0ExTURGY2JtMTBjbmtnUEMwZ1lYTXVhVzUwWldkbGNpaHNaVzVuZEdnb2FWWmhjbk1wTHpFdU1qVXBYRzVjYm1CZ1lDSjkgLS0+XG5cbmBgYHJcblxubnRyZWUgPC0gNTAxXG5tdHJ5IDwtIGFzLmludGVnZXIobGVuZ3RoKGlWYXJzKS8xLjI1KVxuXG5gYGBcblxuPCEtLSBybmItc291cmNlLWVuZCAtLT5cbiJ9 -->

ntree <- 501
mtry <- as.integer(length(iVars)/1.25)



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dVhHNXVkSEpsWlNBOExTQTFNREZjYm0xMGNua2dQQzBnWVhNdWFXNTBaV2RsY2loc1pXNW5kR2dvYVZaaGNuTXBMekV1TWpVcFhHNWNibUJnWUZ4dVlHQmdJbjA9IC0tPlxuXG5gYGByXG5gYGByXG5cbm50cmVlIDwtIDUwMVxubXRyeSA8LSBhcy5pbnRlZ2VyKGxlbmd0aChpVmFycykvMS4yNSlcblxuYGBgXG5gYGBcblxuPCEtLSBybmItc291cmNlLWVuZCAtLT5cbiJ9 -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5udHJlZSA8LSA1MDFcbm10cnkgPC0gYXMuaW50ZWdlcihsZW5ndGgoaVZhcnMpLzEuMjUpXG5cbmBgYFxuYGBgIn0= -->

```r
```r

ntree <- 501
mtry <- as.integer(length(iVars)/1.25)

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


###### Run model

Train preliminary model


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->

nperm <- 5

resultsOutFluct <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutFluct$OOB_RMSE



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5ucGVybSA8LSA1XG5cbnJlc3VsdHNPdXRGbHVjdCA8LSBjcmZSZWcoZGF0YUluPXN0aW1EYXRhTnVtLCBpVmFycz1pVmFycywgZFZhcj1kVmFyLCBzZWVkcz1zZWVkc1sxOjJdLCBudHJlZT1udHJlZSwgbXRyeT1tdHJ5LCBwZXJtSW1wQ29uZFRocmVzPXBlcm1JbXBDb25kVGhyZXMsIG5wZXJtPW5wZXJtLCBtaW5zcGxpdD1taW5zcGxpdCwgbWluYnVja2V0PW1pbmJ1Y2tldClcblxuIyBwcmludCBtb2RlbCBwcmVkaWN0aW9uIHJlc3VsdHNcbnJlc3VsdHNPdXRGbHVjdCRPT0JfUk1TRVxuYGBgXG5gYGAifQ== -->

```r
```r

nperm <- 5

resultsOutFluct <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutFluct$OOB_RMSE
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDAuNjY1MTk2OVxuXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVjbVZ6ZFd4MGMwOTFkRVpzZFdOMEpFOVBRbDlOUVVWY2JtQmdZQ0o5IC0tPlxuXG5gYGByXG5yZXN1bHRzT3V0Rmx1Y3QkT09CX01BRVxuYGBgXG5cbjwhLS0gcm5iLXNvdXJjZS1lbmQgLS0+XG4ifQ== -->

[1] 0.6651969

resultsOutFluct$OOB_MAE



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dWNtVnpkV3gwYzA5MWRFWnNkV04wSkU5UFFsOU5RVVZjYm1CZ1lGeHVZR0JnSW4wPSAtLT5cblxuYGBgclxuYGBgclxucmVzdWx0c091dEZsdWN0JE9PQl9NQUVcbmBgYFxuYGBgXG5cbjwhLS0gcm5iLXNvdXJjZS1lbmQgLS0+XG4ifQ== -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucmVzdWx0c091dEZsdWN0JE9PQl9NQUVcbmBgYFxuYGBgIn0= -->

```r
```r
resultsOutFluct$OOB_MAE
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDAuNTE1NjIxOVxuXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVjbVZ6ZFd4MGMwOTFkRVpzZFdOMEpGSnpjWFZoY21Wa1hHNWdZR0FpZlE9PSAtLT5cblxuYGBgclxucmVzdWx0c091dEZsdWN0JFJzcXVhcmVkXG5gYGBcblxuPCEtLSBybmItc291cmNlLWVuZCAtLT5cbiJ9 -->

[1] 0.5156219

resultsOutFluct$Rsquared



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dWNtVnpkV3gwYzA5MWRFWnNkV04wSkZKemNYVmhjbVZrWEc1Z1lHQmNibUJnWUNKOSAtLT5cblxuYGBgclxuYGBgclxucmVzdWx0c091dEZsdWN0JFJzcXVhcmVkXG5gYGBcbmBgYFxuXG48IS0tIHJuYi1zb3VyY2UtZW5kIC0tPlxuIn0= -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucmVzdWx0c091dEZsdWN0JFJzcXVhcmVkXG5gYGBcbmBgYCJ9 -->

```r
```r
resultsOutFluct$Rsquared
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDAuODQwMjQzOFxuIn0= -->

```
[1] 0.8402438
```



<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Train multiple seeds model


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->

resultsOutFluct <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutFluct$OOB_RMSE



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5yZXN1bHRzT3V0Rmx1Y3QgPC0gbXVsdGlfY3JmUmVnKGRhdGFJbj1zdGltRGF0YU51bSwgaVZhcnM9aVZhcnMsIGRWYXI9ZFZhciwgc2VlZHM9c2VlZHMsIG50cmVlPW50cmVlLCBtdHJ5PW10cnksIHBlcm1JbXBDb25kVGhyZXM9cGVybUltcENvbmRUaHJlcywgbnBlcm09bnBlcm0sIG1pbnNwbGl0PW1pbnNwbGl0LCBtaW5idWNrZXQ9bWluYnVja2V0KVxuXG4jIHByaW50IG1vZGVsIHByZWRpY3Rpb24gcmVzdWx0c1xucmVzdWx0c091dEZsdWN0JE9PQl9STVNFXG5gYGBcbmBgYCJ9 -->

```r
```r

resultsOutFluct <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutFluct$OOB_RMSE
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDAuNjY0OTg0NVxuXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVjbVZ6ZFd4MGMwOTFkRVpzZFdOMEpFOVBRbDlOUVVWY2JtQmdZQ0o5IC0tPlxuXG5gYGByXG5yZXN1bHRzT3V0Rmx1Y3QkT09CX01BRVxuYGBgXG5cbjwhLS0gcm5iLXNvdXJjZS1lbmQgLS0+XG4ifQ== -->

[1] 0.6649845

resultsOutFluct$OOB_MAE



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dWNtVnpkV3gwYzA5MWRFWnNkV04wSkU5UFFsOU5RVVZjYm1CZ1lGeHVZR0JnSW4wPSAtLT5cblxuYGBgclxuYGBgclxucmVzdWx0c091dEZsdWN0JE9PQl9NQUVcbmBgYFxuYGBgXG5cbjwhLS0gcm5iLXNvdXJjZS1lbmQgLS0+XG4ifQ== -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucmVzdWx0c091dEZsdWN0JE9PQl9NQUVcbmBgYFxuYGBgIn0= -->

```r
```r
resultsOutFluct$OOB_MAE
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDAuNTEyMzk5N1xuXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVjbVZ6ZFd4MGMwOTFkRVpzZFdOMEpGSnpjWFZoY21Wa1hHNWdZR0FpZlE9PSAtLT5cblxuYGBgclxucmVzdWx0c091dEZsdWN0JFJzcXVhcmVkXG5gYGBcblxuPCEtLSBybmItc291cmNlLWVuZCAtLT5cbiJ9 -->

[1] 0.5123997

resultsOutFluct$Rsquared



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dWNtVnpkV3gwYzA5MWRFWnNkV04wSkZKemNYVmhjbVZrWEc1Z1lHQmNibUJnWUNKOSAtLT5cblxuYGBgclxuYGBgclxucmVzdWx0c091dEZsdWN0JFJzcXVhcmVkXG5gYGBcbmBgYFxuXG48IS0tIHJuYi1zb3VyY2UtZW5kIC0tPlxuIn0= -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucmVzdWx0c091dEZsdWN0JFJzcXVhcmVkXG5gYGBcbmBgYCJ9 -->

```r
```r
resultsOutFluct$Rsquared
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDAuODM5MDg3OFxuIn0= -->

```
[1] 0.8390878
```



<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->




<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->

# store results
resdAnnoyMnFitAB['Abs fluct', 'RMSE'] <- resultsOutFluct$OOB_RMSE
resdAnnoyMnFitAB['Abs fluct', 'MAE'] <- resultsOutFluct$OOB_MAE
resdAnnoyMnFitAB['Abs fluct', 'Rsquared'] <- resultsOutFluct$Rsquared
resdAnnoyMnPermImpAB$AbsFluct <- resultsOutFluct$conditional_permimp



<!-- rnb-output-end -->

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```r
```r

# store results
resdAnnoyMnFitAB['Abs fluct', 'RMSE'] <- resultsOutFluct$OOB_RMSE
resdAnnoyMnFitAB['Abs fluct', 'MAE'] <- resultsOutFluct$OOB_MAE
resdAnnoyMnFitAB['Abs fluct', 'Rsquared'] <- resultsOutFluct$Rsquared
resdAnnoyMnPermImpAB$AbsFluct <- resultsOutFluct$conditional_permimp

```
```

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###### Plot results


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par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutFluct.conimp <- arrange(resultsOutFluct$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutFluct.conimp) + geom_col(aes(x=factor(rownames(resultsOutFluct.conimp), levels=rownames(resultsOutFluct.conimp)), y=CondPermImp), fill=mycolours[4], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (mean change in annoyance)\) + ggtitle(\Fluctuation strength\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar



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```r
```r
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutFluct.conimp <- arrange(resultsOutFluct$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutFluct.conimp) + geom_col(aes(x=factor(rownames(resultsOutFluct.conimp), levels=rownames(resultsOutFluct.conimp)), y=CondPermImp), fill=mycolours[4], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (mean change in annoyance)\) + ggtitle(\Fluctuation strength\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar
```
```

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```

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<img src=\data:image/png;base64
```



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src="data:image/png;base64,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" />

<!-- rnb-plot-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->

if (saveplots){
  ggsave(filename=\PtsABdAnnoyMnFluctConPermimp.svg\, width=8, height=2.9, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdAnnoyMnFluctConPermimp.svg\)
  
  ggsave(filename=\PtsABdAnnoyMnFluctConPermimp.pdf\, width=8, height=2.9, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdAnnoyMnFluctConPermimp.pdf\)
}



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5pZiAoc2F2ZXBsb3RzKXtcbiAgZ2dzYXZlKGZpbGVuYW1lPVxcUHRzQUJkQW5ub3lNbkZsdWN0Q29uUGVybWltcC5zdmdcXCwgd2lkdGg9OCwgaGVpZ2h0PTIuOSwgcGF0aD1maWxlLnBhdGgob3V0RmlnUGF0aCwgXFxzdmdcXCkpXG4gIHVubGluayhcXFB0c0FCZEFubm95TW5GbHVjdENvblBlcm1pbXAuc3ZnXFwpXG4gIFxuICBnZ3NhdmUoZmlsZW5hbWU9XFxQdHNBQmRBbm5veU1uRmx1Y3RDb25QZXJtaW1wLnBkZlxcLCB3aWR0aD04LCBoZWlnaHQ9Mi45LCBwYXRoPWZpbGUucGF0aChvdXRGaWdQYXRoLCBcXHBkZlxcKSlcbiAgdW5saW5rKFxcUHRzQUJkQW5ub3lNbkZsdWN0Q29uUGVybWltcC5wZGZcXClcbn1cblxuYGBgXG5gYGAifQ== -->

```r
```r

if (saveplots){
  ggsave(filename=\PtsABdAnnoyMnFluctConPermimp.svg\, width=8, height=2.9, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdAnnoyMnFluctConPermimp.svg\)
  
  ggsave(filename=\PtsABdAnnoyMnFluctConPermimp.pdf\, width=8, height=2.9, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdAnnoyMnFluctConPermimp.pdf\)
}

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Selected metric


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVYRzVtYkhWamRGWmhjaUE4TFNCY0lsVkJVMFpzZFdOMFQxWXhNRVY0VFdGNFRGSmNJbHh1WEc1Z1lHQWlmUT09IC0tPlxuXG5gYGByXG5cbmZsdWN0VmFyIDwtIFxcVUFTRmx1Y3RPVjEwRXhNYXhMUlxcXG5cbmBgYFxuXG48IS0tIHJuYi1zb3VyY2UtZW5kIC0tPlxuIn0= -->

fluctVar <- \UASFluctOV10ExMaxLR\



<!-- rnb-output-end -->

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```r
```r

fluctVar <- \UASFluctOV10ExMaxLR\

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


##### Roughness

###### Set variables


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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# Roughness
iVars <- c(absVar, eventVar, ambVar, \UASRoughECMA10ExBin\, \UASRoughECMA05ExBin\, \UASRoughFZ10ExMaxLR\, \UASRoughFZ05ExMaxLR\, \UASRoughDW10ExMaxLR\, \UASRoughDW05ExMaxLR\)
dVar <- \dAnnoyMean\

seeds <- c(4701, 52187, 16589, 65217, 16893)



<!-- rnb-output-end -->

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```r
```r

# Roughness
iVars <- c(absVar, eventVar, ambVar, \UASRoughECMA10ExBin\, \UASRoughECMA05ExBin\, \UASRoughFZ10ExMaxLR\, \UASRoughFZ05ExMaxLR\, \UASRoughDW10ExMaxLR\, \UASRoughDW05ExMaxLR\)
dVar <- \dAnnoyMean\

seeds <- c(4701, 52187, 16589, 65217, 16893)

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


###### Hyperparameter tuning


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)



<!-- rnb-output-end -->

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```r
```r

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

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\" 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" />

<!-- rnb-plot-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Selected hyperparameters


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVYRzV1ZEhKbFpTQThMU0F4TURBeFhHNXRkSEo1SUR3dElHRnpMbWx1ZEdWblpYSW9iR1Z1WjNSb0tHbFdZWEp6S1M4eExqSTFLVnh1WEc1Z1lHQWlmUT09IC0tPlxuXG5gYGByXG5cbm50cmVlIDwtIDEwMDFcbm10cnkgPC0gYXMuaW50ZWdlcihsZW5ndGgoaVZhcnMpLzEuMjUpXG5cbmBgYFxuXG48IS0tIHJuYi1zb3VyY2UtZW5kIC0tPlxuIn0= -->

ntree <- 1001
mtry <- as.integer(length(iVars)/1.25)



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dVhHNXVkSEpsWlNBOExTQXhNREF4WEc1dGRISjVJRHd0SUdGekxtbHVkR1ZuWlhJb2JHVnVaM1JvS0dsV1lYSnpLUzh4TGpJMUtWeHVYRzVnWUdCY2JtQmdZQ0o5IC0tPlxuXG5gYGByXG5gYGByXG5cbm50cmVlIDwtIDEwMDFcbm10cnkgPC0gYXMuaW50ZWdlcihsZW5ndGgoaVZhcnMpLzEuMjUpXG5cbmBgYFxuYGBgXG5cbjwhLS0gcm5iLXNvdXJjZS1lbmQgLS0+XG4ifQ== -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5udHJlZSA8LSAxMDAxXG5tdHJ5IDwtIGFzLmludGVnZXIobGVuZ3RoKGlWYXJzKS8xLjI1KVxuXG5gYGBcbmBgYCJ9 -->

```r
```r

ntree <- 1001
mtry <- as.integer(length(iVars)/1.25)

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


###### Run model

Train preliminary model


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVYRzV1Y0dWeWJTQThMU0ExWEc1Y2JuSmxjM1ZzZEhOUGRYUlNiM1ZuYUNBOExTQmpjbVpTWldjb1pHRjBZVWx1UFhOMGFXMUVZWFJoVG5WdExDQnBWbUZ5Y3oxcFZtRnljeXdnWkZaaGNqMWtWbUZ5TENCelpXVmtjejF6WldWa2Mxc3hPakpkTENCdWRISmxaVDF1ZEhKbFpTd2diWFJ5ZVQxdGRISjVMQ0J3WlhKdFNXMXdRMjl1WkZSb2NtVnpQWEJsY20xSmJYQkRiMjVrVkdoeVpYTXNJRzV3WlhKdFBXNXdaWEp0TENCdGFXNXpjR3hwZEQxdGFXNXpjR3hwZEN3Z2JXbHVZblZqYTJWMFBXMXBibUoxWTJ0bGRDbGNibHh1SXlCd2NtbHVkQ0J0YjJSbGJDQndjbVZrYVdOMGFXOXVJSEpsYzNWc2RITmNibkpsYzNWc2RITlBkWFJTYjNWbmFDUlBUMEpmVWsxVFJWeHVZR0JnSW4wPSAtLT5cblxuYGBgclxuXG5ucGVybSA8LSA1XG5cbnJlc3VsdHNPdXRSb3VnaCA8LSBjcmZSZWcoZGF0YUluPXN0aW1EYXRhTnVtLCBpVmFycz1pVmFycywgZFZhcj1kVmFyLCBzZWVkcz1zZWVkc1sxOjJdLCBudHJlZT1udHJlZSwgbXRyeT1tdHJ5LCBwZXJtSW1wQ29uZFRocmVzPXBlcm1JbXBDb25kVGhyZXMsIG5wZXJtPW5wZXJtLCBtaW5zcGxpdD1taW5zcGxpdCwgbWluYnVja2V0PW1pbmJ1Y2tldClcblxuIyBwcmludCBtb2RlbCBwcmVkaWN0aW9uIHJlc3VsdHNcbnJlc3VsdHNPdXRSb3VnaCRPT0JfUk1TRVxuYGBgXG5cbjwhLS0gcm5iLXNvdXJjZS1lbmQgLS0+XG4ifQ== -->

nperm <- 5

resultsOutRough <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutRough$OOB_RMSE



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


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```r
```r

nperm <- 5

resultsOutRough <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutRough$OOB_RMSE
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDAuNjY4NzEzMVxuXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVjbVZ6ZFd4MGMwOTFkRkp2ZFdkb0pFOVBRbDlOUVVWY2JtQmdZQ0o5IC0tPlxuXG5gYGByXG5yZXN1bHRzT3V0Um91Z2gkT09CX01BRVxuYGBgXG5cbjwhLS0gcm5iLXNvdXJjZS1lbmQgLS0+XG4ifQ== -->

[1] 0.6687131

resultsOutRough$OOB_MAE



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dWNtVnpkV3gwYzA5MWRGSnZkV2RvSkU5UFFsOU5RVVZjYm1CZ1lGeHVZR0JnSW4wPSAtLT5cblxuYGBgclxuYGBgclxucmVzdWx0c091dFJvdWdoJE9PQl9NQUVcbmBgYFxuYGBgXG5cbjwhLS0gcm5iLXNvdXJjZS1lbmQgLS0+XG4ifQ== -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucmVzdWx0c091dFJvdWdoJE9PQl9NQUVcbmBgYFxuYGBgIn0= -->

```r
```r
resultsOutRough$OOB_MAE
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDAuNTE5MjYwM1xuXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVjbVZ6ZFd4MGMwOTFkRkp2ZFdkb0pGSnpjWFZoY21Wa1hHNWdZR0FpZlE9PSAtLT5cblxuYGBgclxucmVzdWx0c091dFJvdWdoJFJzcXVhcmVkXG5gYGBcblxuPCEtLSBybmItc291cmNlLWVuZCAtLT5cbiJ9 -->

[1] 0.5192603

resultsOutRough$Rsquared



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dWNtVnpkV3gwYzA5MWRGSnZkV2RvSkZKemNYVmhjbVZrWEc1Z1lHQmNibUJnWUNKOSAtLT5cblxuYGBgclxuYGBgclxucmVzdWx0c091dFJvdWdoJFJzcXVhcmVkXG5gYGBcbmBgYFxuXG48IS0tIHJuYi1zb3VyY2UtZW5kIC0tPlxuIn0= -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucmVzdWx0c091dFJvdWdoJFJzcXVhcmVkXG5gYGBcbmBgYCJ9 -->

```r
```r
resultsOutRough$Rsquared
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDAuODM3NzM0NFxuIn0= -->

```
[1] 0.8377344
```



<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Train multiple seeds model


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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resultsOutRough <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutRough$OOB_RMSE



<!-- rnb-output-end -->

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<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5yZXN1bHRzT3V0Um91Z2ggPC0gbXVsdGlfY3JmUmVnKGRhdGFJbj1zdGltRGF0YU51bSwgaVZhcnM9aVZhcnMsIGRWYXI9ZFZhciwgc2VlZHM9c2VlZHMsIG50cmVlPW50cmVlLCBtdHJ5PW10cnksIHBlcm1JbXBDb25kVGhyZXM9cGVybUltcENvbmRUaHJlcywgbnBlcm09bnBlcm0sIG1pbnNwbGl0PW1pbnNwbGl0LCBtaW5idWNrZXQ9bWluYnVja2V0KVxuXG4jIHByaW50IG1vZGVsIHByZWRpY3Rpb24gcmVzdWx0c1xucmVzdWx0c091dFJvdWdoJE9PQl9STVNFXG5gYGBcbmBgYCJ9 -->

```r
```r

resultsOutRough <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutRough$OOB_RMSE
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDAuNjY4OTY2N1xuXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVjbVZ6ZFd4MGMwOTFkRkp2ZFdkb0pFOVBRbDlOUVVWY2JtQmdZQ0o5IC0tPlxuXG5gYGByXG5yZXN1bHRzT3V0Um91Z2gkT09CX01BRVxuYGBgXG5cbjwhLS0gcm5iLXNvdXJjZS1lbmQgLS0+XG4ifQ== -->

[1] 0.6689667

resultsOutRough$OOB_MAE



<!-- rnb-output-end -->

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<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucmVzdWx0c091dFJvdWdoJE9PQl9NQUVcbmBgYFxuYGBgIn0= -->

```r
```r
resultsOutRough$OOB_MAE
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDAuNTE5MTcwNFxuXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVjbVZ6ZFd4MGMwOTFkRkp2ZFdkb0pGSnpjWFZoY21Wa1hHNWdZR0FpZlE9PSAtLT5cblxuYGBgclxucmVzdWx0c091dFJvdWdoJFJzcXVhcmVkXG5gYGBcblxuPCEtLSBybmItc291cmNlLWVuZCAtLT5cbiJ9 -->

[1] 0.5191704

resultsOutRough$Rsquared



<!-- rnb-output-end -->

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<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucmVzdWx0c091dFJvdWdoJFJzcXVhcmVkXG5gYGBcbmBgYCJ9 -->

```r
```r
resultsOutRough$Rsquared
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDAuODM4MDEwNlxuIn0= -->

```
[1] 0.8380106
```



<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->



<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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# store results
resdAnnoyMnFitAB['Abs rough', 'RMSE'] <- resultsOutRough$OOB_RMSE
resdAnnoyMnFitAB['Abs rough', 'MAE'] <- resultsOutRough$OOB_MAE
resdAnnoyMnFitAB['Abs rough', 'Rsquared'] <- resultsOutRough$Rsquared
resdAnnoyMnPermImpAB$AbsRough <- resultsOutRough$conditional_permimp



<!-- rnb-output-end -->

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```r
```r
# store results
resdAnnoyMnFitAB['Abs rough', 'RMSE'] <- resultsOutRough$OOB_RMSE
resdAnnoyMnFitAB['Abs rough', 'MAE'] <- resultsOutRough$OOB_MAE
resdAnnoyMnFitAB['Abs rough', 'Rsquared'] <- resultsOutRough$Rsquared
resdAnnoyMnPermImpAB$AbsRough <- resultsOutRough$conditional_permimp

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


###### Plot results


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutRough.conimp <- arrange(resultsOutRough$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutRough.conimp) + geom_col(aes(x=factor(rownames(resultsOutRough.conimp), levels=rownames(resultsOutRough.conimp)), y=CondPermImp), fill=mycolours[5], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (mean change in annoyance)\) + ggtitle(\Roughness\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar



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```r
```r
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutRough.conimp <- arrange(resultsOutRough$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutRough.conimp) + geom_col(aes(x=factor(rownames(resultsOutRough.conimp), levels=rownames(resultsOutRough.conimp)), y=CondPermImp), fill=mycolours[5], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (mean change in annoyance)\) + ggtitle(\Roughness\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar
```
```

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```

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<img src=\data:image/png;base64
```



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src="data:image/png;base64,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/>

<!-- rnb-plot-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->

if (saveplots){
  ggsave(filename=\PtsABdAnnoyMnRoughConPermimp.svg\, width=8, height=2.9, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdAnnoyMnRoughConPermimp.svg\)
  
  ggsave(filename=\PtsABdAnnoyMnRoughConPermimp.pdf\, width=8, height=2.9, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdAnnoyMnRoughConPermimp.pdf\)
}



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5pZiAoc2F2ZXBsb3RzKXtcbiAgZ2dzYXZlKGZpbGVuYW1lPVxcUHRzQUJkQW5ub3lNblJvdWdoQ29uUGVybWltcC5zdmdcXCwgd2lkdGg9OCwgaGVpZ2h0PTIuOSwgcGF0aD1maWxlLnBhdGgob3V0RmlnUGF0aCwgXFxzdmdcXCkpXG4gIHVubGluayhcXFB0c0FCZEFubm95TW5Sb3VnaENvblBlcm1pbXAuc3ZnXFwpXG4gIFxuICBnZ3NhdmUoZmlsZW5hbWU9XFxQdHNBQmRBbm5veU1uUm91Z2hDb25QZXJtaW1wLnBkZlxcLCB3aWR0aD04LCBoZWlnaHQ9Mi45LCBwYXRoPWZpbGUucGF0aChvdXRGaWdQYXRoLCBcXHBkZlxcKSlcbiAgdW5saW5rKFxcUHRzQUJkQW5ub3lNblJvdWdoQ29uUGVybWltcC5wZGZcXClcbn1cblxuXG5gYGBcbmBgYCJ9 -->

```r
```r

if (saveplots){
  ggsave(filename=\PtsABdAnnoyMnRoughConPermimp.svg\, width=8, height=2.9, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdAnnoyMnRoughConPermimp.svg\)
  
  ggsave(filename=\PtsABdAnnoyMnRoughConPermimp.pdf\, width=8, height=2.9, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdAnnoyMnRoughConPermimp.pdf\)
}

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Selected metric


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVYRzV5YjNWbmFGWmhjaUE4TFNCY0lsVkJVMUp2ZFdkb1Jsb3dOVVY0VFdGNFRGSmNJbHh1WEc1Z1lHQWlmUT09IC0tPlxuXG5gYGByXG5cbnJvdWdoVmFyIDwtIFxcVUFTUm91Z2hGWjA1RXhNYXhMUlxcXG5cbmBgYFxuXG48IS0tIHJuYi1zb3VyY2UtZW5kIC0tPlxuIn0= -->

roughVar <- \UASRoughFZ05ExMaxLR\



<!-- rnb-output-end -->

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```r
```r

roughVar <- \UASRoughFZ05ExMaxLR\

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


##### Impulsiveness

###### Set variables


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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# Impulsiveness
iVars <- c(absVar, eventVar, ambVar, \UASImpulsSHMAvgMaxLR\, \UASImpulsSHM05ExMaxLR\, \UASImpulsSHMPowAvgMaxLR\)
dVar <- \dAnnoyMean\

seeds <- c(8495, 59867, 5416, 9843, 86)



<!-- rnb-output-end -->

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```r
```r
# Impulsiveness
iVars <- c(absVar, eventVar, ambVar, \UASImpulsSHMAvgMaxLR\, \UASImpulsSHM05ExMaxLR\, \UASImpulsSHMPowAvgMaxLR\)
dVar <- \dAnnoyMean\

seeds <- c(8495, 59867, 5416, 9843, 86)

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


###### Hyperparameter tuning


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)



<!-- rnb-output-end -->

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```r
```r

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

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```

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<!-- rnb-plot-begin -->

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src="data:image/png;base64,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" />

<!-- rnb-plot-end -->


<!-- rnb-output-end -->

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<!-- rnb-text-begin -->


Selected hyperparameters


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVYRzV1ZEhKbFpTQThMU0F5TlRGY2JtMTBjbmtnUEMwZ1lYTXVhVzUwWldkbGNpaHNaVzVuZEdnb2FWWmhjbk1wTHpFdU56VXBYRzVjYm1CZ1lDSjkgLS0+XG5cbmBgYHJcblxubnRyZWUgPC0gMjUxXG5tdHJ5IDwtIGFzLmludGVnZXIobGVuZ3RoKGlWYXJzKS8xLjc1KVxuXG5gYGBcblxuPCEtLSBybmItc291cmNlLWVuZCAtLT5cbiJ9 -->

ntree <- 251
mtry <- as.integer(length(iVars)/1.75)



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dVhHNXVkSEpsWlNBOExTQXlOVEZjYm0xMGNua2dQQzBnWVhNdWFXNTBaV2RsY2loc1pXNW5kR2dvYVZaaGNuTXBMekV1TnpVcFhHNWNibUJnWUZ4dVlHQmdJbjA9IC0tPlxuXG5gYGByXG5gYGByXG5cbm50cmVlIDwtIDI1MVxubXRyeSA8LSBhcy5pbnRlZ2VyKGxlbmd0aChpVmFycykvMS43NSlcblxuYGBgXG5gYGBcblxuPCEtLSBybmItc291cmNlLWVuZCAtLT5cbiJ9 -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5udHJlZSA8LSAyNTFcbm10cnkgPC0gYXMuaW50ZWdlcihsZW5ndGgoaVZhcnMpLzEuNzUpXG5cbmBgYFxuYGBgIn0= -->

```r
```r

ntree <- 251
mtry <- as.integer(length(iVars)/1.75)

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->



###### Run model

Train preliminary model


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->

nperm <- 5

resultsOutImpuls <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutImpuls$OOB_RMSE



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5ucGVybSA8LSA1XG5cbnJlc3VsdHNPdXRJbXB1bHMgPC0gY3JmUmVnKGRhdGFJbj1zdGltRGF0YU51bSwgaVZhcnM9aVZhcnMsIGRWYXI9ZFZhciwgc2VlZHM9c2VlZHNbMToyXSwgbnRyZWU9bnRyZWUsIG10cnk9bXRyeSwgcGVybUltcENvbmRUaHJlcz1wZXJtSW1wQ29uZFRocmVzLCBucGVybT1ucGVybSwgbWluc3BsaXQ9bWluc3BsaXQsIG1pbmJ1Y2tldD1taW5idWNrZXQpXG5cbiMgcHJpbnQgbW9kZWwgcHJlZGljdGlvbiByZXN1bHRzXG5yZXN1bHRzT3V0SW1wdWxzJE9PQl9STVNFXG5gYGBcbmBgYCJ9 -->

```r
```r

nperm <- 5

resultsOutImpuls <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutImpuls$OOB_RMSE
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDAuNjUyMzg2NFxuXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVjbVZ6ZFd4MGMwOTFkRWx0Y0hWc2N5UlBUMEpmVFVGRlhHNWdZR0FpZlE9PSAtLT5cblxuYGBgclxucmVzdWx0c091dEltcHVscyRPT0JfTUFFXG5gYGBcblxuPCEtLSBybmItc291cmNlLWVuZCAtLT5cbiJ9 -->

[1] 0.6523864

resultsOutImpuls$OOB_MAE



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dWNtVnpkV3gwYzA5MWRFbHRjSFZzY3lSUFQwSmZUVUZGWEc1Z1lHQmNibUJnWUNKOSAtLT5cblxuYGBgclxuYGBgclxucmVzdWx0c091dEltcHVscyRPT0JfTUFFXG5gYGBcbmBgYFxuXG48IS0tIHJuYi1zb3VyY2UtZW5kIC0tPlxuIn0= -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucmVzdWx0c091dEltcHVscyRPT0JfTUFFXG5gYGBcbmBgYCJ9 -->

```r
```r
resultsOutImpuls$OOB_MAE
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDAuNTEzNzcxMlxuXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVjbVZ6ZFd4MGMwOTFkRWx0Y0hWc2N5UlNjM0YxWVhKbFpGeHVZR0JnSW4wPSAtLT5cblxuYGBgclxucmVzdWx0c091dEltcHVscyRSc3F1YXJlZFxuYGBgXG5cbjwhLS0gcm5iLXNvdXJjZS1lbmQgLS0+XG4ifQ== -->

[1] 0.5137712

resultsOutImpuls$Rsquared



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dWNtVnpkV3gwYzA5MWRFbHRjSFZzY3lSU2MzRjFZWEpsWkZ4dVlHQmdYRzVnWUdBaWZRPT0gLS0+XG5cbmBgYHJcbmBgYHJcbnJlc3VsdHNPdXRJbXB1bHMkUnNxdWFyZWRcbmBgYFxuYGBgXG5cbjwhLS0gcm5iLXNvdXJjZS1lbmQgLS0+XG4ifQ== -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucmVzdWx0c091dEltcHVscyRSc3F1YXJlZFxuYGBgXG5gYGAifQ== -->

```r
```r
resultsOutImpuls$Rsquared
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDAuODc2Nzk4M1xuIn0= -->

```
[1] 0.8767983
```



<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Train multiple seeds model


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->

resultsOutImpuls <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutImpuls$OOB_RMSE



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5yZXN1bHRzT3V0SW1wdWxzIDwtIG11bHRpX2NyZlJlZyhkYXRhSW49c3RpbURhdGFOdW0sIGlWYXJzPWlWYXJzLCBkVmFyPWRWYXIsIHNlZWRzPXNlZWRzLCBudHJlZT1udHJlZSwgbXRyeT1tdHJ5LCBwZXJtSW1wQ29uZFRocmVzPXBlcm1JbXBDb25kVGhyZXMsIG5wZXJtPW5wZXJtLCBtaW5zcGxpdD1taW5zcGxpdCwgbWluYnVja2V0PW1pbmJ1Y2tldClcblxuIyBwcmludCBtb2RlbCBwcmVkaWN0aW9uIHJlc3VsdHNcbnJlc3VsdHNPdXRJbXB1bHMkT09CX1JNU0VcbmBgYFxuYGBgIn0= -->

```r
```r

resultsOutImpuls <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutImpuls$OOB_RMSE
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDAuNjU4MjUyMlxuXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVjbVZ6ZFd4MGMwOTFkRWx0Y0hWc2N5UlBUMEpmVFVGRlhHNWdZR0FpZlE9PSAtLT5cblxuYGBgclxucmVzdWx0c091dEltcHVscyRPT0JfTUFFXG5gYGBcblxuPCEtLSBybmItc291cmNlLWVuZCAtLT5cbiJ9 -->

[1] 0.6582522

resultsOutImpuls$OOB_MAE



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dWNtVnpkV3gwYzA5MWRFbHRjSFZzY3lSUFQwSmZUVUZGWEc1Z1lHQmNibUJnWUNKOSAtLT5cblxuYGBgclxuYGBgclxucmVzdWx0c091dEltcHVscyRPT0JfTUFFXG5gYGBcbmBgYFxuXG48IS0tIHJuYi1zb3VyY2UtZW5kIC0tPlxuIn0= -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucmVzdWx0c091dEltcHVscyRPT0JfTUFFXG5gYGBcbmBgYCJ9 -->

```r
```r
resultsOutImpuls$OOB_MAE
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDAuNTI0MzE0NVxuXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVjbVZ6ZFd4MGMwOTFkRWx0Y0hWc2N5UlNjM0YxWVhKbFpGeHVZR0JnSW4wPSAtLT5cblxuYGBgclxucmVzdWx0c091dEltcHVscyRSc3F1YXJlZFxuYGBgXG5cbjwhLS0gcm5iLXNvdXJjZS1lbmQgLS0+XG4ifQ== -->

[1] 0.5243145

resultsOutImpuls$Rsquared



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dWNtVnpkV3gwYzA5MWRFbHRjSFZzY3lSU2MzRjFZWEpsWkZ4dVlHQmdYRzVnWUdBaWZRPT0gLS0+XG5cbmBgYHJcbmBgYHJcbnJlc3VsdHNPdXRJbXB1bHMkUnNxdWFyZWRcbmBgYFxuYGBgXG5cbjwhLS0gcm5iLXNvdXJjZS1lbmQgLS0+XG4ifQ== -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucmVzdWx0c091dEltcHVscyRSc3F1YXJlZFxuYGBgXG5gYGAifQ== -->

```r
```r
resultsOutImpuls$Rsquared
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDAuODc1NTY2NFxuIn0= -->

```
[1] 0.8755664
```



<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->



<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->

# store results
resdAnnoyMnFitAB['Abs impuls', 'RMSE'] <- resultsOutImpuls$OOB_RMSE
resdAnnoyMnFitAB['Abs impuls', 'MAE'] <- resultsOutImpuls$OOB_MAE
resdAnnoyMnFitAB['Abs impuls', 'Rsquared'] <- resultsOutImpuls$Rsquared
resdAnnoyMnPermImpAB$AbsImpuls <- resultsOutImpuls$conditional_permimp



<!-- rnb-output-end -->

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```r
```r

# store results
resdAnnoyMnFitAB['Abs impuls', 'RMSE'] <- resultsOutImpuls$OOB_RMSE
resdAnnoyMnFitAB['Abs impuls', 'MAE'] <- resultsOutImpuls$OOB_MAE
resdAnnoyMnFitAB['Abs impuls', 'Rsquared'] <- resultsOutImpuls$Rsquared
resdAnnoyMnPermImpAB$AbsImpuls <- resultsOutImpuls$conditional_permimp

```
```

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###### Plot results


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<!-- rnb-chunk-begin -->


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par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutImpuls.conimp <- arrange(resultsOutImpuls$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutImpuls.conimp) + geom_col(aes(x=factor(rownames(resultsOutImpuls.conimp), levels=rownames(resultsOutImpuls.conimp)), y=CondPermImp), fill=mycolours[6], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (mean change in annoyance)\) + ggtitle(\Impulsiveness\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar



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```r
```r
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutImpuls.conimp <- arrange(resultsOutImpuls$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutImpuls.conimp) + geom_col(aes(x=factor(rownames(resultsOutImpuls.conimp), levels=rownames(resultsOutImpuls.conimp)), y=CondPermImp), fill=mycolours[6], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (mean change in annoyance)\) + ggtitle(\Impulsiveness\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar
```
```

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```

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<img src=\data:image/png;base64
```



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src="data:image/png;base64,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/>

<!-- rnb-plot-end -->


<!-- rnb-output-end -->

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if (saveplots){
  ggsave(filename=\PtsABdAnnoyMnImpulsConPermimp.svg\, width=8, height=2, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdAnnoyMnImpulsConPermimp.svg\)
  
  ggsave(filename=\PtsABdAnnoyMnImpulsConPermimp.pdf\, width=8, height=2, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdAnnoyMnImpulsConPermimp.pdf\)
}



<!-- rnb-output-end -->

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```r
```r

if (saveplots){
  ggsave(filename=\PtsABdAnnoyMnImpulsConPermimp.svg\, width=8, height=2, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdAnnoyMnImpulsConPermimp.svg\)
  
  ggsave(filename=\PtsABdAnnoyMnImpulsConPermimp.pdf\, width=8, height=2, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdAnnoyMnImpulsConPermimp.pdf\)
}

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Selected metric


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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impulsVar <- \UASImpulsSHMAvgMaxLR\



<!-- rnb-output-end -->

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```r
```r

impulsVar <- \UASImpulsSHMAvgMaxLR\

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


#### SQM and loudness comparison

Now the highest importance SQMs are ranked against each other, controlling for UAS loudness and ambient LAeq.

##### Include tonal loudness

###### Set variables


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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iVars <- c(absVar, eventVar, ambVar, sharpVar, tonLdVar, fluctVar, roughVar, impulsVar)
dVar <- \dAnnoyMean\

seeds <- c(70498, 4, 14986, 453, 864)



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5pVmFycyA8LSBjKGFic1ZhciwgZXZlbnRWYXIsIGFtYlZhciwgc2hhcnBWYXIsIHRvbkxkVmFyLCBmbHVjdFZhciwgcm91Z2hWYXIsIGltcHVsc1ZhcilcbmRWYXIgPC0gXFxkQW5ub3lNZWFuXFxcblxuc2VlZHMgPC0gYyg3MDQ5OCwgNCwgMTQ5ODYsIDQ1MywgODY0KVxuXG5gYGBcbmBgYCJ9 -->

```r
```r

iVars <- c(absVar, eventVar, ambVar, sharpVar, tonLdVar, fluctVar, roughVar, impulsVar)
dVar <- \dAnnoyMean\

seeds <- c(70498, 4, 14986, 453, 864)

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


###### Hyperparameter tuning


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)



<!-- rnb-output-end -->

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<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5wIDwtIG10cnlUdW5lKGRhdGFJbj1zdGltRGF0YU51bSwgaVZhcnM9aVZhcnMsIGRWYXI9ZFZhciwgc2VlZD1zZWVkc1sxOjJdLFxuICAgICAgICAgICAgIG50cmVlcz1udHJlZXMsIG1pbnNwbGl0PW1pbnNwbGl0LCBtaW5idWNrZXQ9bWluYnVja2V0KVxuYGBgXG5gYGAifQ== -->

```r
```r

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

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-->

```

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\" 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" />

<!-- rnb-plot-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Selected hyperparameters


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVYRzV1ZEhKbFpTQThMU0EwTURBeFhHNXRkSEo1SUR3dElHRnpMbWx1ZEdWblpYSW9iR1Z1WjNSb0tHbFdZWEp6S1M4eExqSTFLVnh1WEc1Z1lHQWlmUT09IC0tPlxuXG5gYGByXG5cbm50cmVlIDwtIDQwMDFcbm10cnkgPC0gYXMuaW50ZWdlcihsZW5ndGgoaVZhcnMpLzEuMjUpXG5cbmBgYFxuXG48IS0tIHJuYi1zb3VyY2UtZW5kIC0tPlxuIn0= -->

ntree <- 4001
mtry <- as.integer(length(iVars)/1.25)



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dVhHNXVkSEpsWlNBOExTQTBNREF4WEc1dGRISjVJRHd0SUdGekxtbHVkR1ZuWlhJb2JHVnVaM1JvS0dsV1lYSnpLUzh4TGpJMUtWeHVYRzVnWUdCY2JtQmdZQ0o5IC0tPlxuXG5gYGByXG5gYGByXG5cbm50cmVlIDwtIDQwMDFcbm10cnkgPC0gYXMuaW50ZWdlcihsZW5ndGgoaVZhcnMpLzEuMjUpXG5cbmBgYFxuYGBgXG5cbjwhLS0gcm5iLXNvdXJjZS1lbmQgLS0+XG4ifQ== -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5udHJlZSA8LSA0MDAxXG5tdHJ5IDwtIGFzLmludGVnZXIobGVuZ3RoKGlWYXJzKS8xLjI1KVxuXG5gYGBcbmBgYCJ9 -->

```r
```r

ntree <- 4001
mtry <- as.integer(length(iVars)/1.25)

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


###### Run model

Train preliminary model


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->

nperm <- 5

resultsOutSQMs1 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSQMs1$OOB_RMSE



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5ucGVybSA8LSA1XG5cbnJlc3VsdHNPdXRTUU1zMSA8LSBjcmZSZWcoZGF0YUluPXN0aW1EYXRhTnVtLCBpVmFycz1pVmFycywgZFZhcj1kVmFyLCBzZWVkcz1zZWVkc1sxOjJdLCBudHJlZT1udHJlZSwgbXRyeT1tdHJ5LCBwZXJtSW1wQ29uZFRocmVzPXBlcm1JbXBDb25kVGhyZXMsIG5wZXJtPW5wZXJtLCBtaW5zcGxpdD1taW5zcGxpdCwgbWluYnVja2V0PW1pbmJ1Y2tldClcblxuIyBwcmludCBtb2RlbCBwcmVkaWN0aW9uIHJlc3VsdHNcbnJlc3VsdHNPdXRTUU1zMSRPT0JfUk1TRVxuYGBgXG5gYGAifQ== -->

```r
```r

nperm <- 5

resultsOutSQMs1 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSQMs1$OOB_RMSE
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDAuNjY1MTI0MVxuXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVjbVZ6ZFd4MGMwOTFkRk5SVFhNeEpFOVBRbDlOUVVWY2JtQmdZQ0o5IC0tPlxuXG5gYGByXG5yZXN1bHRzT3V0U1FNczEkT09CX01BRVxuYGBgXG5cbjwhLS0gcm5iLXNvdXJjZS1lbmQgLS0+XG4ifQ== -->

[1] 0.6651241

resultsOutSQMs1$OOB_MAE



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dWNtVnpkV3gwYzA5MWRGTlJUWE14SkU5UFFsOU5RVVZjYm1CZ1lGeHVZR0JnSW4wPSAtLT5cblxuYGBgclxuYGBgclxucmVzdWx0c091dFNRTXMxJE9PQl9NQUVcbmBgYFxuYGBgXG5cbjwhLS0gcm5iLXNvdXJjZS1lbmQgLS0+XG4ifQ== -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucmVzdWx0c091dFNRTXMxJE9PQl9NQUVcbmBgYFxuYGBgIn0= -->

```r
```r
resultsOutSQMs1$OOB_MAE
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDAuNTIxMzMwNVxuXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVjbVZ6ZFd4MGMwOTFkRk5SVFhNeEpGSnpjWFZoY21Wa1hHNWdZR0FpZlE9PSAtLT5cblxuYGBgclxucmVzdWx0c091dFNRTXMxJFJzcXVhcmVkXG5gYGBcblxuPCEtLSBybmItc291cmNlLWVuZCAtLT5cbiJ9 -->

[1] 0.5213305

resultsOutSQMs1$Rsquared



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dWNtVnpkV3gwYzA5MWRGTlJUWE14SkZKemNYVmhjbVZrWEc1Z1lHQmNibUJnWUNKOSAtLT5cblxuYGBgclxuYGBgclxucmVzdWx0c091dFNRTXMxJFJzcXVhcmVkXG5gYGBcbmBgYFxuXG48IS0tIHJuYi1zb3VyY2UtZW5kIC0tPlxuIn0= -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucmVzdWx0c091dFNRTXMxJFJzcXVhcmVkXG5gYGBcbmBgYCJ9 -->

```r
```r
resultsOutSQMs1$Rsquared
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDAuODQxOTcyN1xuIn0= -->

```
[1] 0.8419727
```



<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Train multiple seeds model


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->

resultsOutSQMs1 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSQMs1$OOB_RMSE



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5yZXN1bHRzT3V0U1FNczEgPC0gbXVsdGlfY3JmUmVnKGRhdGFJbj1zdGltRGF0YU51bSwgaVZhcnM9aVZhcnMsIGRWYXI9ZFZhciwgc2VlZHM9c2VlZHMsIG50cmVlPW50cmVlLCBtdHJ5PW10cnksIHBlcm1JbXBDb25kVGhyZXM9cGVybUltcENvbmRUaHJlcywgbnBlcm09bnBlcm0sIG1pbnNwbGl0PW1pbnNwbGl0LCBtaW5idWNrZXQ9bWluYnVja2V0KVxuXG4jIHByaW50IG1vZGVsIHByZWRpY3Rpb24gcmVzdWx0c1xucmVzdWx0c091dFNRTXMxJE9PQl9STVNFXG5gYGBcbmBgYCJ9 -->

```r
```r

resultsOutSQMs1 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSQMs1$OOB_RMSE
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDAuNjY3NzU4NVxuXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVjbVZ6ZFd4MGMwOTFkRk5SVFhNeEpFOVBRbDlOUVVWY2JtQmdZQ0o5IC0tPlxuXG5gYGByXG5yZXN1bHRzT3V0U1FNczEkT09CX01BRVxuYGBgXG5cbjwhLS0gcm5iLXNvdXJjZS1lbmQgLS0+XG4ifQ== -->

[1] 0.6677585

resultsOutSQMs1$OOB_MAE



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dWNtVnpkV3gwYzA5MWRGTlJUWE14SkU5UFFsOU5RVVZjYm1CZ1lGeHVZR0JnSW4wPSAtLT5cblxuYGBgclxuYGBgclxucmVzdWx0c091dFNRTXMxJE9PQl9NQUVcbmBgYFxuYGBgXG5cbjwhLS0gcm5iLXNvdXJjZS1lbmQgLS0+XG4ifQ== -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucmVzdWx0c091dFNRTXMxJE9PQl9NQUVcbmBgYFxuYGBgIn0= -->

```r
```r
resultsOutSQMs1$OOB_MAE
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDAuNTIxMzk5NFxuXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVjbVZ6ZFd4MGMwOTFkRk5SVFhNeEpGSnpjWFZoY21Wa1hHNWdZR0FpZlE9PSAtLT5cblxuYGBgclxucmVzdWx0c091dFNRTXMxJFJzcXVhcmVkXG5gYGBcblxuPCEtLSBybmItc291cmNlLWVuZCAtLT5cbiJ9 -->

[1] 0.5213994

resultsOutSQMs1$Rsquared



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dWNtVnpkV3gwYzA5MWRGTlJUWE14SkZKemNYVmhjbVZrWEc1Z1lHQmNibUJnWUNKOSAtLT5cblxuYGBgclxuYGBgclxucmVzdWx0c091dFNRTXMxJFJzcXVhcmVkXG5gYGBcbmBgYFxuXG48IS0tIHJuYi1zb3VyY2UtZW5kIC0tPlxuIn0= -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucmVzdWx0c091dFNRTXMxJFJzcXVhcmVkXG5gYGBcbmBgYCJ9 -->

```r
```r
resultsOutSQMs1$Rsquared
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDAuODM5OTAyOVxuIn0= -->

```
[1] 0.8399029
```



<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->



<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->

# store results
resdAnnoyMnFitAB['Abs SQMs inc tonal loud', 'RMSE'] <- resultsOutSQMs1$OOB_RMSE
resdAnnoyMnFitAB['Abs SQMs inc tonal loud', 'MAE'] <- resultsOutSQMs1$OOB_MAE
resdAnnoyMnFitAB['Abs SQMs inc tonal loud', 'Rsquared'] <- resultsOutSQMs1$Rsquared
resdAnnoyMnPermImpAB$AbsSQMs1 <- resultsOutSQMs1$conditional_permimp



<!-- rnb-output-end -->

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```r
```r

# store results
resdAnnoyMnFitAB['Abs SQMs inc tonal loud', 'RMSE'] <- resultsOutSQMs1$OOB_RMSE
resdAnnoyMnFitAB['Abs SQMs inc tonal loud', 'MAE'] <- resultsOutSQMs1$OOB_MAE
resdAnnoyMnFitAB['Abs SQMs inc tonal loud', 'Rsquared'] <- resultsOutSQMs1$Rsquared
resdAnnoyMnPermImpAB$AbsSQMs1 <- resultsOutSQMs1$conditional_permimp

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


###### Plot results


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutSQMs1.conimp <- arrange(resultsOutSQMs1$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutSQMs1.conimp) + geom_col(aes(x=factor(rownames(resultsOutSQMs1.conimp), levels=rownames(resultsOutSQMs1.conimp)), y=CondPermImp), fill=mycolours[7], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (mean change in annoyance)\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 1.3))
pBar



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```r
```r
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutSQMs1.conimp <- arrange(resultsOutSQMs1$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutSQMs1.conimp) + geom_col(aes(x=factor(rownames(resultsOutSQMs1.conimp), levels=rownames(resultsOutSQMs1.conimp)), y=CondPermImp), fill=mycolours[7], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (mean change in annoyance)\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 1.3))
pBar
```
```

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```

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<img src=\data:image/png;base64
```



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src="data:image/png;base64,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" />

<!-- rnb-plot-end -->


<!-- rnb-output-end -->

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if (saveplots){
  ggsave(filename=\PtsABdAnnoyMnAbsSQMsTonLdConPermimp.svg\, width=8, height=2.4, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdAnnoyMnAbsSQMsTonLdConPermimp.svg\)
  
  ggsave(filename=\PtsABdAnnoyMnAbsSQMsTonLdConPermimp.pdf\, width=8, height=2.4, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdAnnoyMnAbsSQMsTonLdConPermimp.pdf\)
}



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


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```r
```r

if (saveplots){
  ggsave(filename=\PtsABdAnnoyMnAbsSQMsTonLdConPermimp.svg\, width=8, height=2.4, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdAnnoyMnAbsSQMsTonLdConPermimp.svg\)
  
  ggsave(filename=\PtsABdAnnoyMnAbsSQMsTonLdConPermimp.pdf\, width=8, height=2.4, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdAnnoyMnAbsSQMsTonLdConPermimp.pdf\)
}

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


##### Exclude tonal loudness

###### Set variables


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->

iVars <- c(absVar, eventVar, ambVar, sharpVar, tonalVar, fluctVar, roughVar, impulsVar)
dVar <- \dAnnoyMean\

seeds <- c(546, 57203, 270835, 60592, 8094)



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


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```r
```r

iVars <- c(absVar, eventVar, ambVar, sharpVar, tonalVar, fluctVar, roughVar, impulsVar)
dVar <- \dAnnoyMean\

seeds <- c(546, 57203, 270835, 60592, 8094)

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


###### Hyperparameter tuning


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)



<!-- rnb-output-end -->

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<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5wIDwtIG10cnlUdW5lKGRhdGFJbj1zdGltRGF0YU51bSwgaVZhcnM9aVZhcnMsIGRWYXI9ZFZhciwgc2VlZD1zZWVkc1sxOjJdLFxuICAgICAgICAgICAgIG50cmVlcz1udHJlZXMsIG1pbnNwbGl0PW1pbnNwbGl0LCBtaW5idWNrZXQ9bWluYnVja2V0KVxuYGBgXG5gYGAifQ== -->

```r
```r

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

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-->

```

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\" 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" />

<!-- rnb-plot-end -->


<!-- rnb-output-end -->

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<!-- rnb-text-begin -->


Selected hyperparameters


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVYRzV1ZEhKbFpTQThMU0ExTURGY2JtMTBjbmtnUEMwZ1lYTXVhVzUwWldkbGNpaHNaVzVuZEdnb2FWWmhjbk1wTHpFdU5TbGNibHh1WUdCZ0luMD0gLS0+XG5cbmBgYHJcblxubnRyZWUgPC0gNTAxXG5tdHJ5IDwtIGFzLmludGVnZXIobGVuZ3RoKGlWYXJzKS8xLjUpXG5cbmBgYFxuXG48IS0tIHJuYi1zb3VyY2UtZW5kIC0tPlxuIn0= -->

ntree <- 501
mtry <- as.integer(length(iVars)/1.5)



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dVhHNXVkSEpsWlNBOExTQTFNREZjYm0xMGNua2dQQzBnWVhNdWFXNTBaV2RsY2loc1pXNW5kR2dvYVZaaGNuTXBMekV1TlNsY2JseHVZR0JnWEc1Z1lHQWlmUT09IC0tPlxuXG5gYGByXG5gYGByXG5cbm50cmVlIDwtIDUwMVxubXRyeSA8LSBhcy5pbnRlZ2VyKGxlbmd0aChpVmFycykvMS41KVxuXG5gYGBcbmBgYFxuXG48IS0tIHJuYi1zb3VyY2UtZW5kIC0tPlxuIn0= -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5udHJlZSA8LSA1MDFcbm10cnkgPC0gYXMuaW50ZWdlcihsZW5ndGgoaVZhcnMpLzEuNSlcblxuYGBgXG5gYGAifQ== -->

```r
```r

ntree <- 501
mtry <- as.integer(length(iVars)/1.5)

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


###### Run model

Train preliminary model


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->

nperm <- 5

resultsOutSQMs2 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSQMs2$OOB_RMSE



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5ucGVybSA8LSA1XG5cbnJlc3VsdHNPdXRTUU1zMiA8LSBjcmZSZWcoZGF0YUluPXN0aW1EYXRhTnVtLCBpVmFycz1pVmFycywgZFZhcj1kVmFyLCBzZWVkcz1zZWVkc1sxOjJdLCBudHJlZT1udHJlZSwgbXRyeT1tdHJ5LCBwZXJtSW1wQ29uZFRocmVzPXBlcm1JbXBDb25kVGhyZXMsIG5wZXJtPW5wZXJtLCBtaW5zcGxpdD1taW5zcGxpdCwgbWluYnVja2V0PW1pbmJ1Y2tldClcblxuIyBwcmludCBtb2RlbCBwcmVkaWN0aW9uIHJlc3VsdHNcbnJlc3VsdHNPdXRTUU1zMiRPT0JfUk1TRVxuYGBgXG5gYGAifQ== -->

```r
```r

nperm <- 5

resultsOutSQMs2 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSQMs2$OOB_RMSE
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDAuNjU1NjU1MVxuXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVjbVZ6ZFd4MGMwOTFkRk5SVFhNeUpFOVBRbDlOUVVWY2JtQmdZQ0o5IC0tPlxuXG5gYGByXG5yZXN1bHRzT3V0U1FNczIkT09CX01BRVxuYGBgXG5cbjwhLS0gcm5iLXNvdXJjZS1lbmQgLS0+XG4ifQ== -->

[1] 0.6556551

resultsOutSQMs2$OOB_MAE



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dWNtVnpkV3gwYzA5MWRGTlJUWE15SkU5UFFsOU5RVVZjYm1CZ1lGeHVZR0JnSW4wPSAtLT5cblxuYGBgclxuYGBgclxucmVzdWx0c091dFNRTXMyJE9PQl9NQUVcbmBgYFxuYGBgXG5cbjwhLS0gcm5iLXNvdXJjZS1lbmQgLS0+XG4ifQ== -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucmVzdWx0c091dFNRTXMyJE9PQl9NQUVcbmBgYFxuYGBgIn0= -->

```r
```r
resultsOutSQMs2$OOB_MAE
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDAuNTE4NDgxXG5cbjwhLS0gcm5iLXNvdXJjZS1iZWdpbiBleUprWVhSaElqb2lZR0JnY2x4dWNtVnpkV3gwYzA5MWRGTlJUWE15SkZKemNYVmhjbVZrWEc1Z1lHQWlmUT09IC0tPlxuXG5gYGByXG5yZXN1bHRzT3V0U1FNczIkUnNxdWFyZWRcbmBgYFxuXG48IS0tIHJuYi1zb3VyY2UtZW5kIC0tPlxuIn0= -->

[1] 0.518481

resultsOutSQMs2$Rsquared



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dWNtVnpkV3gwYzA5MWRGTlJUWE15SkZKemNYVmhjbVZrWEc1Z1lHQmNibUJnWUNKOSAtLT5cblxuYGBgclxuYGBgclxucmVzdWx0c091dFNRTXMyJFJzcXVhcmVkXG5gYGBcbmBgYFxuXG48IS0tIHJuYi1zb3VyY2UtZW5kIC0tPlxuIn0= -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucmVzdWx0c091dFNRTXMyJFJzcXVhcmVkXG5gYGBcbmBgYCJ9 -->

```r
```r
resultsOutSQMs2$Rsquared
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDAuODUxMDc1M1xuIn0= -->

```
[1] 0.8510753
```



<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Train multiple seeds model


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->

resultsOutSQMs2 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSQMs2$OOB_RMSE



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5yZXN1bHRzT3V0U1FNczIgPC0gbXVsdGlfY3JmUmVnKGRhdGFJbj1zdGltRGF0YU51bSwgaVZhcnM9aVZhcnMsIGRWYXI9ZFZhciwgc2VlZHM9c2VlZHMsIG50cmVlPW50cmVlLCBtdHJ5PW10cnksIHBlcm1JbXBDb25kVGhyZXM9cGVybUltcENvbmRUaHJlcywgbnBlcm09bnBlcm0sIG1pbnNwbGl0PW1pbnNwbGl0LCBtaW5idWNrZXQ9bWluYnVja2V0KVxuXG4jIHByaW50IG1vZGVsIHByZWRpY3Rpb24gcmVzdWx0c1xucmVzdWx0c091dFNRTXMyJE9PQl9STVNFXG5gYGBcbmBgYCJ9 -->

```r
```r

resultsOutSQMs2 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSQMs2$OOB_RMSE
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDAuNjU3Njc2NVxuXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVjbVZ6ZFd4MGMwOTFkRk5SVFhNeUpFOVBRbDlOUVVWY2JtQmdZQ0o5IC0tPlxuXG5gYGByXG5yZXN1bHRzT3V0U1FNczIkT09CX01BRVxuYGBgXG5cbjwhLS0gcm5iLXNvdXJjZS1lbmQgLS0+XG4ifQ== -->

[1] 0.6576765

resultsOutSQMs2$OOB_MAE



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dWNtVnpkV3gwYzA5MWRGTlJUWE15SkU5UFFsOU5RVVZjYm1CZ1lGeHVZR0JnSW4wPSAtLT5cblxuYGBgclxuYGBgclxucmVzdWx0c091dFNRTXMyJE9PQl9NQUVcbmBgYFxuYGBgXG5cbjwhLS0gcm5iLXNvdXJjZS1lbmQgLS0+XG4ifQ== -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucmVzdWx0c091dFNRTXMyJE9PQl9NQUVcbmBgYFxuYGBgIn0= -->

```r
```r
resultsOutSQMs2$OOB_MAE
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDAuNTE1NDcxNlxuXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVjbVZ6ZFd4MGMwOTFkRk5SVFhNeUpGSnpjWFZoY21Wa1hHNWdZR0FpZlE9PSAtLT5cblxuYGBgclxucmVzdWx0c091dFNRTXMyJFJzcXVhcmVkXG5gYGBcblxuPCEtLSBybmItc291cmNlLWVuZCAtLT5cbiJ9 -->

[1] 0.5154716

resultsOutSQMs2$Rsquared



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dWNtVnpkV3gwYzA5MWRGTlJUWE15SkZKemNYVmhjbVZrWEc1Z1lHQmNibUJnWUNKOSAtLT5cblxuYGBgclxuYGBgclxucmVzdWx0c091dFNRTXMyJFJzcXVhcmVkXG5gYGBcbmBgYFxuXG48IS0tIHJuYi1zb3VyY2UtZW5kIC0tPlxuIn0= -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucmVzdWx0c091dFNRTXMyJFJzcXVhcmVkXG5gYGBcbmBgYCJ9 -->

```r
```r
resultsOutSQMs2$Rsquared
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDAuODQ5OTc0OVxuIn0= -->

```
[1] 0.8499749
```



<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->



<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->

# store results
resdAnnoyMnFitAB['Abs SQMs no tonal loud', 'RMSE'] <- resultsOutSQMs2$OOB_RMSE
resdAnnoyMnFitAB['Abs SQMs no tonal loud', 'RMSE'] <- resultsOutSQMs2$OOB_MAE
resdAnnoyMnFitAB['Abs SQMs no tonal loud', 'Rsquared'] <- resultsOutSQMs2$Rsquared
resdAnnoyMnPermImpAB$AbsSQMs2 <- resultsOutSQMs2$conditional_permimp



<!-- rnb-output-end -->

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```r
```r

# store results
resdAnnoyMnFitAB['Abs SQMs no tonal loud', 'RMSE'] <- resultsOutSQMs2$OOB_RMSE
resdAnnoyMnFitAB['Abs SQMs no tonal loud', 'RMSE'] <- resultsOutSQMs2$OOB_MAE
resdAnnoyMnFitAB['Abs SQMs no tonal loud', 'Rsquared'] <- resultsOutSQMs2$Rsquared
resdAnnoyMnPermImpAB$AbsSQMs2 <- resultsOutSQMs2$conditional_permimp

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

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###### Plot results


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutSQMs2.conimp <- arrange(resultsOutSQMs2$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutSQMs2.conimp) + geom_col(aes(x=factor(rownames(resultsOutSQMs2.conimp), levels=rownames(resultsOutSQMs2.conimp)), y=CondPermImp), fill=mycolours[7], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (mean change in annoyance)\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 1.3))
pBar



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```r
```r
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutSQMs2.conimp <- arrange(resultsOutSQMs2$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutSQMs2.conimp) + geom_col(aes(x=factor(rownames(resultsOutSQMs2.conimp), levels=rownames(resultsOutSQMs2.conimp)), y=CondPermImp), fill=mycolours[7], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (mean change in annoyance)\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 1.3))
pBar
```
```

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```

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<img src=\data:image/png;base64
```



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<!-- rnb-plot-end -->


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  unlink(\PtsABdAnnoyMnAbsSQMsNoTonLdConPermimp.svg\)
  
  ggsave(filename=\PtsABdAnnoyMnAbsSQMsNoTonLdConPermimp.pdf\, width=8, height=2.4, path=file.path(outFigPath, \pdf\))
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}



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```r
```r

if (saveplots){
  ggsave(filename=\PtsABdAnnoyMnAbsSQMsNoTonLdConPermimp.svg\, width=8, height=2.4, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdAnnoyMnAbsSQMsNoTonLdConPermimp.svg\)
  
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}

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


### Difference variables

Next, the difference metrics are analysed, 

#### Remove ambient only stimuli - OMITTED

Here, the 'ambient only' stimuli are removed, as the analysis cannot handle missing values for dB metrics.


<!-- rnb-text-end -->


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# stimDataNum <- stimDataNum[complete.cases(stimDataNum),]
# stimDataNum$UASLAeq <- as.numeric(stimDataNum$UASLAeq)
# stimDataNum$SNRlevel <- as.numeric(stimDataNum$SNRlevel)



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```r
```r
# stimDataNum <- stimDataNum[complete.cases(stimDataNum),]
# stimDataNum$UASLAeq <- as.numeric(stimDataNum$UASLAeq)
# stimDataNum$SNRlevel <- as.numeric(stimDataNum$SNRlevel)
```
```

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<!-- rnb-text-begin -->


#### Set variables


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iVars <- c(names(stimDataNum)[which(colnames(stimDataNum)==\LAeqLAF90diff\):
                               which(colnames(stimDataNum)==\dImpulsSHM05ExMaxLR\)],
           \UASEvents\, \SNRlevel\, \IntermitRatioMaxLR\)
dVar <- \dAnnoyMean\

seeds <- c(568392, 498, 4089, 78132, 741809)



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```r
```r

iVars <- c(names(stimDataNum)[which(colnames(stimDataNum)==\LAeqLAF90diff\):
                               which(colnames(stimDataNum)==\dImpulsSHM05ExMaxLR\)],
           \UASEvents\, \SNRlevel\, \IntermitRatioMaxLR\)
dVar <- \dAnnoyMean\

seeds <- c(568392, 498, 4089, 78132, 741809)

```
```

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<!-- rnb-text-begin -->


#### Hyperparameter tuning


<!-- rnb-text-end -->


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p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)



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```r
```r

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
```
```

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<!-- rnb-output-end -->

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\" />\n\n<!-- rnb-plot-end -->\n"} -->


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" />

<!-- rnb-plot-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Selected hyperparameters


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVYRzV1ZEhKbFpTQThMU0ExTURGY2JtMTBjbmtnUEMwZ1lYTXVhVzUwWldkbGNpaHNaVzVuZEdnb2FWWmhjbk1wTHpFdU5TbGNibHh1WUdCZ0luMD0gLS0+XG5cbmBgYHJcblxubnRyZWUgPC0gNTAxXG5tdHJ5IDwtIGFzLmludGVnZXIobGVuZ3RoKGlWYXJzKS8xLjUpXG5cbmBgYFxuXG48IS0tIHJuYi1zb3VyY2UtZW5kIC0tPlxuIn0= -->

ntree <- 501
mtry <- as.integer(length(iVars)/1.5)



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dVhHNXVkSEpsWlNBOExTQTFNREZjYm0xMGNua2dQQzBnWVhNdWFXNTBaV2RsY2loc1pXNW5kR2dvYVZaaGNuTXBMekV1TlNsY2JseHVZR0JnWEc1Z1lHQWlmUT09IC0tPlxuXG5gYGByXG5gYGByXG5cbm50cmVlIDwtIDUwMVxubXRyeSA8LSBhcy5pbnRlZ2VyKGxlbmd0aChpVmFycykvMS41KVxuXG5gYGBcbmBgYFxuXG48IS0tIHJuYi1zb3VyY2UtZW5kIC0tPlxuIn0= -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5udHJlZSA8LSA1MDFcbm10cnkgPC0gYXMuaW50ZWdlcihsZW5ndGgoaVZhcnMpLzEuNSlcblxuYGBgXG5gYGAifQ== -->

```r
```r

ntree <- 501
mtry <- as.integer(length(iVars)/1.5)

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


#### Run model

Train preliminary model


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->

nperm <- 10

resultsOutDiffs <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutDiffs$OOB_RMSE



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5ucGVybSA8LSAxMFxuXG5yZXN1bHRzT3V0RGlmZnMgPC0gY3JmUmVnKGRhdGFJbj1zdGltRGF0YU51bSwgaVZhcnM9aVZhcnMsIGRWYXI9ZFZhciwgc2VlZHM9c2VlZHNbMToyXSwgbnRyZWU9bnRyZWUsIG10cnk9bXRyeSwgcGVybUltcENvbmRUaHJlcz1wZXJtSW1wQ29uZFRocmVzLCBucGVybT1ucGVybSwgbWluc3BsaXQ9bWluc3BsaXQsIG1pbmJ1Y2tldD1taW5idWNrZXQpXG5cbiMgcHJpbnQgbW9kZWwgcHJlZGljdGlvbiByZXN1bHRzXG5yZXN1bHRzT3V0RGlmZnMkT09CX1JNU0VcbmBgYFxuYGBgIn0= -->

```r
```r

nperm <- 10

resultsOutDiffs <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutDiffs$OOB_RMSE
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDAuNTMxMjU0MlxuXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVjbVZ6ZFd4MGMwOTFkRVJwWm1aekpFOVBRbDlOUVVWY2JtQmdZQ0o5IC0tPlxuXG5gYGByXG5yZXN1bHRzT3V0RGlmZnMkT09CX01BRVxuYGBgXG5cbjwhLS0gcm5iLXNvdXJjZS1lbmQgLS0+XG4ifQ== -->

[1] 0.5312542

resultsOutDiffs$OOB_MAE



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dWNtVnpkV3gwYzA5MWRFUnBabVp6SkU5UFFsOU5RVVZjYm1CZ1lGeHVZR0JnSW4wPSAtLT5cblxuYGBgclxuYGBgclxucmVzdWx0c091dERpZmZzJE9PQl9NQUVcbmBgYFxuYGBgXG5cbjwhLS0gcm5iLXNvdXJjZS1lbmQgLS0+XG4ifQ== -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucmVzdWx0c091dERpZmZzJE9PQl9NQUVcbmBgYFxuYGBgIn0= -->

```r
```r
resultsOutDiffs$OOB_MAE
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDAuNDEyODU5MVxuXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVjbVZ6ZFd4MGMwOTFkRVJwWm1aekpGSnpjWFZoY21Wa1hHNWdZR0FpZlE9PSAtLT5cblxuYGBgclxucmVzdWx0c091dERpZmZzJFJzcXVhcmVkXG5gYGBcblxuPCEtLSBybmItc291cmNlLWVuZCAtLT5cbiJ9 -->

[1] 0.4128591

resultsOutDiffs$Rsquared



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dWNtVnpkV3gwYzA5MWRFUnBabVp6SkZKemNYVmhjbVZrWEc1Z1lHQmNibUJnWUNKOSAtLT5cblxuYGBgclxuYGBgclxucmVzdWx0c091dERpZmZzJFJzcXVhcmVkXG5gYGBcbmBgYFxuXG48IS0tIHJuYi1zb3VyY2UtZW5kIC0tPlxuIn0= -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucmVzdWx0c091dERpZmZzJFJzcXVhcmVkXG5gYGBcbmBgYCJ9 -->

```r
```r
resultsOutDiffs$Rsquared
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDAuODg5NDEyN1xuIn0= -->

```
[1] 0.8894127
```



<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Train multiple seeds model


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->

resultsOutDiffs <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutDiffs$OOB_RMSE



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5yZXN1bHRzT3V0RGlmZnMgPC0gbXVsdGlfY3JmUmVnKGRhdGFJbj1zdGltRGF0YU51bSwgaVZhcnM9aVZhcnMsIGRWYXI9ZFZhciwgc2VlZHM9c2VlZHMsIG50cmVlPW50cmVlLCBtdHJ5PW10cnksIHBlcm1JbXBDb25kVGhyZXM9cGVybUltcENvbmRUaHJlcywgbnBlcm09bnBlcm0sIG1pbnNwbGl0PW1pbnNwbGl0LCBtaW5idWNrZXQ9bWluYnVja2V0KVxuXG4jIHByaW50IG1vZGVsIHByZWRpY3Rpb24gcmVzdWx0c1xucmVzdWx0c091dERpZmZzJE9PQl9STVNFXG5gYGBcbmBgYCJ9 -->

```r
```r

resultsOutDiffs <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutDiffs$OOB_RMSE
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDAuNTI5NTk3MVxuXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVjbVZ6ZFd4MGMwOTFkRVJwWm1aekpFOVBRbDlOUVVWY2JtQmdZQ0o5IC0tPlxuXG5gYGByXG5yZXN1bHRzT3V0RGlmZnMkT09CX01BRVxuYGBgXG5cbjwhLS0gcm5iLXNvdXJjZS1lbmQgLS0+XG4ifQ== -->

[1] 0.5295971

resultsOutDiffs$OOB_MAE



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dWNtVnpkV3gwYzA5MWRFUnBabVp6SkU5UFFsOU5RVVZjYm1CZ1lGeHVZR0JnSW4wPSAtLT5cblxuYGBgclxuYGBgclxucmVzdWx0c091dERpZmZzJE9PQl9NQUVcbmBgYFxuYGBgXG5cbjwhLS0gcm5iLXNvdXJjZS1lbmQgLS0+XG4ifQ== -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucmVzdWx0c091dERpZmZzJE9PQl9NQUVcbmBgYFxuYGBgIn0= -->

```r
```r
resultsOutDiffs$OOB_MAE
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDAuNDEwOTQzOVxuXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVjbVZ6ZFd4MGMwOTFkRVJwWm1aekpGSnpjWFZoY21Wa1hHNWdZR0FpZlE9PSAtLT5cblxuYGBgclxucmVzdWx0c091dERpZmZzJFJzcXVhcmVkXG5gYGBcblxuPCEtLSBybmItc291cmNlLWVuZCAtLT5cbiJ9 -->

[1] 0.4109439

resultsOutDiffs$Rsquared



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dWNtVnpkV3gwYzA5MWRFUnBabVp6SkZKemNYVmhjbVZrWEc1Z1lHQmNibUJnWUNKOSAtLT5cblxuYGBgclxuYGBgclxucmVzdWx0c091dERpZmZzJFJzcXVhcmVkXG5gYGBcbmBgYFxuXG48IS0tIHJuYi1zb3VyY2UtZW5kIC0tPlxuIn0= -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucmVzdWx0c091dERpZmZzJFJzcXVhcmVkXG5gYGBcbmBgYCJ9 -->

```r
```r
resultsOutDiffs$Rsquared
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDAuODkwMjE3MVxuIn0= -->

```
[1] 0.8902171
```



<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->



<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->

# store results
resdAnnoyMnFitAB['Diff vars', 'RMSE'] <- resultsOutDiffs$OOB_RMSE
resdAnnoyMnFitAB['Diff vars', 'MAE'] <- resultsOutDiffs$OOB_MAE
resdAnnoyMnFitAB['Diff vars', 'Rsquared'] <- resultsOutDiffs$Rsquared
resdAnnoyMnPermImpAB$DiffVars <- resultsOutDiffs$conditional_permimp



<!-- rnb-output-end -->

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```r
```r

# store results
resdAnnoyMnFitAB['Diff vars', 'RMSE'] <- resultsOutDiffs$OOB_RMSE
resdAnnoyMnFitAB['Diff vars', 'MAE'] <- resultsOutDiffs$OOB_MAE
resdAnnoyMnFitAB['Diff vars', 'Rsquared'] <- resultsOutDiffs$Rsquared
resdAnnoyMnPermImpAB$DiffVars <- resultsOutDiffs$conditional_permimp

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


#### Plot results


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<!-- rnb-chunk-begin -->


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par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutDiffs.conimp <- arrange(resultsOutDiffs$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutDiffs.conimp) + geom_col(aes(x=factor(rownames(resultsOutDiffs.conimp), levels=rownames(resultsOutDiffs.conimp)), y=CondPermImp), fill=mycolours[8], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (mean change in annoyance)\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar



<!-- rnb-output-end -->

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```r
```r
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutDiffs.conimp <- arrange(resultsOutDiffs$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutDiffs.conimp) + geom_col(aes(x=factor(rownames(resultsOutDiffs.conimp), levels=rownames(resultsOutDiffs.conimp)), y=CondPermImp), fill=mycolours[8], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (mean change in annoyance)\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar
```
```

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```

<!-- rnb-plot-begin eyJoZWlnaHQiOjEwLCJ3aWR0aCI6OCwic2l6ZV9iZWhhdmlvciI6MSwiY29uZGl0aW9ucyI6W119 -->

<img src=\data:image/png;base64
```



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/>

<!-- rnb-plot-end -->


<!-- rnb-output-end -->

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if (saveplots){
  ggsave(filename=\PtsABdAnnoyMnDiffVarsConPermimp.svg\, width=8, height=10, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdAnnoyMnDiffVarsConPermimp.svg\)

  ggsave(filename=\PtsABdAnnoyMnDiffVarsConPermimp.pdf\, width=8, height=10, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdAnnoyMnDiffVarsConPermimp.pdf\)
}



<!-- rnb-output-end -->

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```r
```r

if (saveplots){
  ggsave(filename=\PtsABdAnnoyMnDiffVarsConPermimp.svg\, width=8, height=10, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdAnnoyMnDiffVarsConPermimp.svg\)

  ggsave(filename=\PtsABdAnnoyMnDiffVarsConPermimp.pdf\, width=8, height=10, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdAnnoyMnDiffVarsConPermimp.pdf\)
}

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


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# Plot only positive values
resultsOutDiffs.conimpPtv <- resultsOutDiffs.conimp |>
                                        rownames_to_column('Metric') |>
                                            filter_if(is.numeric, all_vars(. > 0)) |>
                                                  column_to_rownames('Metric')

pBar <- ggplot(resultsOutDiffs.conimpPtv) + geom_col(aes(x=factor(rownames(resultsOutDiffs.conimpPtv), levels=rownames(resultsOutDiffs.conimpPtv)), y=CondPermImp), fill=mycolours[8], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (mean change in annoyance)\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar



<!-- rnb-output-end -->

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```r
```r

# Plot only positive values
resultsOutDiffs.conimpPtv <- resultsOutDiffs.conimp |>
                                        rownames_to_column('Metric') |>
                                            filter_if(is.numeric, all_vars(. > 0)) |>
                                                  column_to_rownames('Metric')

pBar <- ggplot(resultsOutDiffs.conimpPtv) + geom_col(aes(x=factor(rownames(resultsOutDiffs.conimpPtv), levels=rownames(resultsOutDiffs.conimpPtv)), y=CondPermImp), fill=mycolours[8], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (mean change in annoyance)\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar
```
```

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```

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<img src=\data:image/png;base64
```



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\" 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/>

<!-- rnb-plot-end -->


<!-- rnb-output-end -->

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if (saveplots){
  ggsave(filename=\PtsABdAnnoyMnDiffVarsConPermimpPtv.svg\, width=8, height=8, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdAnnoyMnDiffVarsConPermimpPtv.svg\)

  ggsave(filename=\PtsABdAnnoyMnDiffVarsConPermimpPtv.pdf\, width=8, height=8, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdAnnoyMnDiffVarsConPermimpPtv.pdf\)
}



<!-- rnb-output-end -->

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```r
```r

if (saveplots){
  ggsave(filename=\PtsABdAnnoyMnDiffVarsConPermimpPtv.svg\, width=8, height=8, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdAnnoyMnDiffVarsConPermimpPtv.svg\)

  ggsave(filename=\PtsABdAnnoyMnDiffVarsConPermimpPtv.pdf\, width=8, height=8, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdAnnoyMnDiffVarsConPermimpPtv.pdf\)
}

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-chunk-begin -->


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# Plot only values within 1% of the maximum
resultsOutDiffs.conimp1pc <- resultsOutDiffs.conimp |>
                                        rownames_to_column('Metric') |>
                                            filter_if(is.numeric, all_vars(. > max(resultsOutDiffs.conimp)/100)) |>
                                                  column_to_rownames('Metric')

pBar <- ggplot(resultsOutDiffs.conimp1pc) + geom_col(aes(x=factor(rownames(resultsOutDiffs.conimp1pc), levels=rownames(resultsOutDiffs.conimp1pc)), y=CondPermImp), fill=mycolours[8], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (mean change in annoyance)\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar



<!-- rnb-output-end -->

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```r
```r

# Plot only values within 1% of the maximum
resultsOutDiffs.conimp1pc <- resultsOutDiffs.conimp |>
                                        rownames_to_column('Metric') |>
                                            filter_if(is.numeric, all_vars(. > max(resultsOutDiffs.conimp)/100)) |>
                                                  column_to_rownames('Metric')

pBar <- ggplot(resultsOutDiffs.conimp1pc) + geom_col(aes(x=factor(rownames(resultsOutDiffs.conimp1pc), levels=rownames(resultsOutDiffs.conimp1pc)), y=CondPermImp), fill=mycolours[8], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (mean change in annoyance)\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

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```

<!-- rnb-plot-begin eyJoZWlnaHQiOjQsIndpZHRoIjo4LCJzaXplX2JlaGF2aW9yIjoxLCJjb25kaXRpb25zIjpbXX0= -->

<img src=\data:image/png;base64
```



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<!-- rnb-plot-end -->


<!-- rnb-output-end -->

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if (saveplots){
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  unlink(\PtsABdAnnoyMnDiffVarsConPermimp1pc.svg\)

  ggsave(filename=\PtsABdAnnoyMnDiffVarsConPermimp1pc.pdf\, width=8, height=4, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdAnnoyMnDiffVarsConPermimp1pc.pdf\)
}



<!-- rnb-output-end -->

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```r
```r

if (saveplots){
  ggsave(filename=\PtsABdAnnoyMnDiffVarsConPermimp1pc.svg\, width=8, height=4, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdAnnoyMnDiffVarsConPermimp1pc.svg\)

  ggsave(filename=\PtsABdAnnoyMnDiffVarsConPermimp1pc.pdf\, width=8, height=4, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdAnnoyMnDiffVarsConPermimp1pc.pdf\)
}

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->



<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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# Plot only values within 1% of the maximum
resultsOutDiffs.conimp1pc <- resultsOutDiffs.conimp |>
                                        rownames_to_column('Metric') |>
                                            filter_if(is.numeric, all_vars(. > max(resultsOutDiffs.conimp)/100)) |>
                                                  column_to_rownames('Metric')

pBar <- ggplot(resultsOutDiffs.conimp1pc) + geom_col(aes(x=factor(rownames(resultsOutDiffs.conimp1pc), levels=rownames(resultsOutDiffs.conimp1pc)), y=CondPermImp), fill=mycolours[8], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation\nimportance (mean change in annoyance)\) + theme(text = element_text(family = \serif\), axis.title.x=element_text(family = \serif\, size=9), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar



<!-- rnb-output-end -->

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```r
```r

# Plot only values within 1% of the maximum
resultsOutDiffs.conimp1pc <- resultsOutDiffs.conimp |>
                                        rownames_to_column('Metric') |>
                                            filter_if(is.numeric, all_vars(. > max(resultsOutDiffs.conimp)/100)) |>
                                                  column_to_rownames('Metric')

pBar <- ggplot(resultsOutDiffs.conimp1pc) + geom_col(aes(x=factor(rownames(resultsOutDiffs.conimp1pc), levels=rownames(resultsOutDiffs.conimp1pc)), y=CondPermImp), fill=mycolours[8], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation\nimportance (mean change in annoyance)\) + theme(text = element_text(family = \serif\), axis.title.x=element_text(family = \serif\, size=9), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

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```

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<img src=\data:image/png;base64
```



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<!-- rnb-plot-end -->


<!-- rnb-output-end -->

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filename = \PtsABdAnnoyMnDiffVarsConPermimp1pcNw\

if (saveplots){
  ggsave(filename=paste(filename, \.svg\), width=4, height=4, path=file.path(outFigPath, \svg\))
  unlink(paste(filename, \.svg\))

  ggsave(filename=paste(filename, \.pdf\), width=4, height=4, path=file.path(outFigPath, \pdf\))
  unlink(paste(filename, \.svg\))
}



<!-- rnb-output-end -->

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```r
```r

filename = \PtsABdAnnoyMnDiffVarsConPermimp1pcNw\

if (saveplots){
  ggsave(filename=paste(filename, \.svg\), width=4, height=4, path=file.path(outFigPath, \svg\))
  unlink(paste(filename, \.svg\))

  ggsave(filename=paste(filename, \.pdf\), width=4, height=4, path=file.path(outFigPath, \pdf\))
  unlink(paste(filename, \.svg\))
}

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->



Selected metric


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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diffVar <- \LAELAF50diff\



<!-- rnb-output-end -->

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<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5kaWZmVmFyIDwtIFxcTEFFTEFGNTBkaWZmXFxcblxuYGBgXG5gYGAifQ== -->

```r
```r

diffVar <- \LAELAF50diff\

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


### dSQM analysis

The ambient-only stimuli are NOT replaced here, as the difference analysis includes dB metrics.

#### Individual SQMs

##### dSharpness

###### Set variables


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->

iVars <- c(diffVar, eventVar, \dSharpAurISO3PowAvgMaxLR\, \dSharpAurISO305ExMaxLR\, \dSharpAurSHMPowAvgMaxLR\, \dSharpAurSHM05ExMaxLR\)
dVar <- \dAnnoyMean\

seeds <- c(84194, 905, 64815, 928054, 625091)



<!-- rnb-output-end -->

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```r
```r

iVars <- c(diffVar, eventVar, \dSharpAurISO3PowAvgMaxLR\, \dSharpAurISO305ExMaxLR\, \dSharpAurSHMPowAvgMaxLR\, \dSharpAurSHM05ExMaxLR\)
dVar <- \dAnnoyMean\

seeds <- c(84194, 905, 64815, 928054, 625091)

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


###### Hyperparameter tuning


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVYRzV3SUR3dElHMTBjbmxVZFc1bEtHUmhkR0ZKYmoxemRHbHRSR0YwWVU1MWJTd2dhVlpoY25NOWFWWmhjbk1zSUdSV1lYSTlaRlpoY2l3Z2MyVmxaRDF6WldWa2Mxc3hPakpkTEZ4dUlDQWdJQ0FnSUNBZ0lDQWdJRzUwY21WbGN6MXVkSEpsWlhNc0lHMXBibk53YkdsMFBXMXBibk53YkdsMExDQnRhVzVpZFdOclpYUTliV2x1WW5WamEyVjBLVnh1Y0Z4dVhHNWdZR0FpZlE9PSAtLT5cblxuYGBgclxuXG5wIDwtIG10cnlUdW5lKGRhdGFJbj1zdGltRGF0YU51bSwgaVZhcnM9aVZhcnMsIGRWYXI9ZFZhciwgc2VlZD1zZWVkc1sxOjJdLFxuICAgICAgICAgICAgIG50cmVlcz1udHJlZXMsIG1pbnNwbGl0PW1pbnNwbGl0LCBtaW5idWNrZXQ9bWluYnVja2V0KVxucFxuXG5gYGBcblxuPCEtLSBybmItc291cmNlLWVuZCAtLT5cbiJ9 -->

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


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```r
```r

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Selected hyperparameters


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVYRzV1ZEhKbFpTQThMVEkxTVZ4dWJYUnllU0E4TFNCaGN5NXBiblJsWjJWeUtHeGxibWQwYUNocFZtRnljeWt2TVM0M05TbGNibHh1WUdCZ0luMD0gLS0+XG5cbmBgYHJcblxubnRyZWUgPC0yNTFcbm10cnkgPC0gYXMuaW50ZWdlcihsZW5ndGgoaVZhcnMpLzEuNzUpXG5cbmBgYFxuXG48IS0tIHJuYi1zb3VyY2UtZW5kIC0tPlxuIn0= -->

ntree <-251
mtry <- as.integer(length(iVars)/1.75)



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dVhHNXVkSEpsWlNBOExUSTFNVnh1YlhSeWVTQThMU0JoY3k1cGJuUmxaMlZ5S0d4bGJtZDBhQ2hwVm1GeWN5a3ZNUzQzTlNsY2JseHVZR0JnWEc1Z1lHQWlmUT09IC0tPlxuXG5gYGByXG5gYGByXG5cbm50cmVlIDwtMjUxXG5tdHJ5IDwtIGFzLmludGVnZXIobGVuZ3RoKGlWYXJzKS8xLjc1KVxuXG5gYGBcbmBgYFxuXG48IS0tIHJuYi1zb3VyY2UtZW5kIC0tPlxuIn0= -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5udHJlZSA8LTI1MVxubXRyeSA8LSBhcy5pbnRlZ2VyKGxlbmd0aChpVmFycykvMS43NSlcblxuYGBgXG5gYGAifQ== -->

```r
```r

ntree <-251
mtry <- as.integer(length(iVars)/1.75)

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


###### Run model

Train preliminary model


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->

nperm <- 5

resultsOutSharp <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSharp$OOB_RMSE
resultsOutSharp$OOB_MAE
resultsOutSharp$Rsquared



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


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```r
```r

nperm <- 5

resultsOutSharp <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSharp$OOB_RMSE
resultsOutSharp$OOB_MAE
resultsOutSharp$Rsquared

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Train multiple seeds model


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->

resultsOutSharp <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSharp$OOB_RMSE
resultsOutSharp$OOB_MAE
resultsOutSharp$Rsquared



<!-- rnb-output-end -->

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```r
```r

resultsOutSharp <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSharp$OOB_RMSE
resultsOutSharp$OOB_MAE
resultsOutSharp$Rsquared

```
```

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# store results
resdAnnoyMnFitAB['Diff sharp', 'RMSE'] <- resultsOutSharp$OOB_RMSE
resdAnnoyMnFitAB['Diff sharp', 'MAE'] <- resultsOutSharp$OOB_MAE
resdAnnoyMnFitAB['Diff sharp', 'Rsquared'] <- resultsOutSharp$Rsquared
resdAnnoyMnPermImpAB$DiffSharp <- resultsOutSharp$conditional_permimp



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```r
```r
# store results
resdAnnoyMnFitAB['Diff sharp', 'RMSE'] <- resultsOutSharp$OOB_RMSE
resdAnnoyMnFitAB['Diff sharp', 'MAE'] <- resultsOutSharp$OOB_MAE
resdAnnoyMnFitAB['Diff sharp', 'Rsquared'] <- resultsOutSharp$Rsquared
resdAnnoyMnPermImpAB$DiffSharp <- resultsOutSharp$conditional_permimp

```
```

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###### Plot results


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par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutSharp.conimp <- arrange(resultsOutSharp$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutSharp.conimp) + geom_col(aes(x=factor(rownames(resultsOutSharp.conimp), levels=rownames(resultsOutSharp.conimp)), y=CondPermImp), fill=mycolours[2], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (mean change in annoyance)\) + ggtitle(\dSharpness\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename=\PtsABdAnnoyMndSharpConPermimp.svg\, width=8, height=2.6, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdAnnoyMndSharpConPermimp.svg\)
  
  ggsave(filename=\PtsABdAnnoyMndSharpConPermimp.pdf\, width=8, height=2.6, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdAnnoyMndSharpConPermimp.pdf\)
}



<!-- rnb-output-end -->

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```r
```r
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutSharp.conimp <- arrange(resultsOutSharp$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutSharp.conimp) + geom_col(aes(x=factor(rownames(resultsOutSharp.conimp), levels=rownames(resultsOutSharp.conimp)), y=CondPermImp), fill=mycolours[2], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (mean change in annoyance)\) + ggtitle(\dSharpness\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename=\PtsABdAnnoyMndSharpConPermimp.svg\, width=8, height=2.6, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdAnnoyMndSharpConPermimp.svg\)
  
  ggsave(filename=\PtsABdAnnoyMndSharpConPermimp.pdf\, width=8, height=2.6, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdAnnoyMndSharpConPermimp.pdf\)
}

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Selected metric


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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dSharpVar <- \dSharpAurISO3PowAvgMaxLR\



<!-- rnb-output-end -->

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```r
```r

dSharpVar <- \dSharpAurISO3PowAvgMaxLR\

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


##### dTonal loudness and dtonality

###### Set variables


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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iVars <- c(diffVar, eventVar, \dTonalECMAAvgMaxLR\, \dTonalSHMInt05ExMaxLR\, \dTonalSHMIntAvgMaxLR\, \dTonalECMA05ExMaxLR\, \dTonLdECMAPowAvgBin\, \dTonLdECMA05ExBin\)
dVar <- \dAnnoyMean\

seeds <- c(561684, 104798, 1536, 48, 48561)



<!-- rnb-output-end -->

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```r
```r

iVars <- c(diffVar, eventVar, \dTonalECMAAvgMaxLR\, \dTonalSHMInt05ExMaxLR\, \dTonalSHMIntAvgMaxLR\, \dTonalECMA05ExMaxLR\, \dTonLdECMAPowAvgBin\, \dTonLdECMA05ExBin\)
dVar <- \dAnnoyMean\

seeds <- c(561684, 104798, 1536, 48, 48561)

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


###### Hyperparameter tuning


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p



<!-- rnb-output-end -->

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```r
```r

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Selected hyperparameters


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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ntree <- 1501
mtry <- as.integer(length(iVars)/1.25)



<!-- rnb-output-end -->

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```r
```r

ntree <- 1501
mtry <- as.integer(length(iVars)/1.25)

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


###### Run model

Train preliminary model


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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# Tonality with tonal loudness

nperm <- 5

resultsOutTonal1 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutTonal1$OOB_RMSE
resultsOutTonal1$OOB_MAE
resultsOutTonal1$Rsquared



<!-- rnb-output-end -->

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```r
```r
# Tonality with tonal loudness

nperm <- 5

resultsOutTonal1 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutTonal1$OOB_RMSE
resultsOutTonal1$OOB_MAE
resultsOutTonal1$Rsquared
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->

Train multiple seeds model


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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# Tonality with tonal loudness

resultsOutTonal1 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutTonal1$OOB_RMSE
resultsOutTonal1$OOB_MAE
resultsOutTonal1$Rsquared



<!-- rnb-output-end -->

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```r
```r
# Tonality with tonal loudness

resultsOutTonal1 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutTonal1$OOB_RMSE
resultsOutTonal1$OOB_MAE
resultsOutTonal1$Rsquared

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->



<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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# store results
resdAnnoyMnFitAB['Diff tonal inc loud', 'RMSE'] <- resultsOutTonal1$OOB_RMSE
resdAnnoyMnFitAB['Diff tonal inc loud', 'MAE'] <- resultsOutTonal1$OOB_MAE
resdAnnoyMnFitAB['Diff tonal inc loud', 'Rsquared'] <- resultsOutTonal1$Rsquared
resdAnnoyMnPermImpAB$DiffTonal1 <- resultsOutTonal1$conditional_permimp



<!-- rnb-output-end -->

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```r
```r
# store results
resdAnnoyMnFitAB['Diff tonal inc loud', 'RMSE'] <- resultsOutTonal1$OOB_RMSE
resdAnnoyMnFitAB['Diff tonal inc loud', 'MAE'] <- resultsOutTonal1$OOB_MAE
resdAnnoyMnFitAB['Diff tonal inc loud', 'Rsquared'] <- resultsOutTonal1$Rsquared
resdAnnoyMnPermImpAB$DiffTonal1 <- resultsOutTonal1$conditional_permimp

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

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<!-- rnb-text-begin -->


###### Plot results


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutTonal1.conimp <- arrange(resultsOutTonal1$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutTonal1.conimp) + geom_col(aes(x=factor(rownames(resultsOutTonal1.conimp), levels=rownames(resultsOutTonal1.conimp)), y=CondPermImp), fill=mycolours[3], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (mean change in annoyance)\) + ggtitle(\dTonality inc. dtonal loudness\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 2.2))
pBar

if (saveplots){
  ggsave(filename=\PtsABdAnnoyMndTonalLdConPermimp.svg\, width=8, height=2.6, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdAnnoyMndTonalLdConPermimp.svg\)
  
  ggsave(filename=\PtsABdAnnoyMndTonalLdConPermimp.pdf\, width=8, height=2.6, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdAnnoyMndTonalLdConPermimp.pdf\)
}



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```r
```r

par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutTonal1.conimp <- arrange(resultsOutTonal1$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutTonal1.conimp) + geom_col(aes(x=factor(rownames(resultsOutTonal1.conimp), levels=rownames(resultsOutTonal1.conimp)), y=CondPermImp), fill=mycolours[3], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (mean change in annoyance)\) + ggtitle(\dTonality inc. dtonal loudness\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 2.2))
pBar

if (saveplots){
  ggsave(filename=\PtsABdAnnoyMndTonalLdConPermimp.svg\, width=8, height=2.6, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdAnnoyMndTonalLdConPermimp.svg\)
  
  ggsave(filename=\PtsABdAnnoyMndTonalLdConPermimp.pdf\, width=8, height=2.6, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdAnnoyMndTonalLdConPermimp.pdf\)
}

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Selected metric


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVYRzVrVkc5dVRHUldZWElnUEMwZ1hDSmtWRzl1VEdSRlEwMUJVRzkzUVhablFtbHVYQ0pjYmx4dVlHQmdJbjA9IC0tPlxuXG5gYGByXG5cbmRUb25MZFZhciA8LSBcXGRUb25MZEVDTUFQb3dBdmdCaW5cXFxuXG5gYGBcblxuPCEtLSBybmItc291cmNlLWVuZCAtLT5cbiJ9 -->

dTonLdVar <- \dTonLdECMAPowAvgBin\



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dVhHNWtWRzl1VEdSV1lYSWdQQzBnWEZ4a1ZHOXVUR1JGUTAxQlVHOTNRWFpuUW1sdVhGeGNibHh1WUdCZ1hHNWdZR0FpZlE9PSAtLT5cblxuYGBgclxuYGBgclxuXG5kVG9uTGRWYXIgPC0gXFxkVG9uTGRFQ01BUG93QXZnQmluXFxcblxuYGBgXG5gYGBcblxuPCEtLSBybmItc291cmNlLWVuZCAtLT5cbiJ9 -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5kVG9uTGRWYXIgPC0gXFxkVG9uTGRFQ01BUG93QXZnQmluXFxcblxuYGBgXG5gYGAifQ== -->

```r
```r

dTonLdVar <- \dTonLdECMAPowAvgBin\

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


##### dTonality without dtonal loudness

###### Set variables


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->

iVars <- c(diffVar, eventVar, \dTonalECMAAvgMaxLR\, \dTonalSHMInt05ExMaxLR\, \dTonalSHMIntAvgMaxLR\, \dTonalECMA05ExMaxLR\)
dVar <- \dAnnoyMean\

seeds <- c(410865, 2954, 70812, 203, 7984)



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dVhHNXBWbUZ5Y3lBOExTQmpLR1JwWm1aV1lYSXNJR1YyWlc1MFZtRnlMQ0JjWEdSVWIyNWhiRVZEVFVGQmRtZE5ZWGhNVWx4Y0xDQmNYR1JVYjI1aGJGTklUVWx1ZERBMVJYaE5ZWGhNVWx4Y0xDQmNYR1JVYjI1aGJGTklUVWx1ZEVGMlowMWhlRXhTWEZ3c0lGeGNaRlJ2Ym1Gc1JVTk5RVEExUlhoTllYaE1VbHhjS1Z4dVpGWmhjaUE4TFNCY1hHUkJibTV2ZVUxbFlXNWNYRnh1WEc1elpXVmtjeUE4TFNCaktEUXhNRGcyTlN3Z01qazFOQ3dnTnpBNE1USXNJREl3TXl3Z056azROQ2xjYmx4dVlHQmdYRzVnWUdBaWZRPT0gLS0+XG5cbmBgYHJcbmBgYHJcblxuaVZhcnMgPC0gYyhkaWZmVmFyLCBldmVudFZhciwgXFxkVG9uYWxFQ01BQXZnTWF4TFJcXCwgXFxkVG9uYWxTSE1JbnQwNUV4TWF4TFJcXCwgXFxkVG9uYWxTSE1JbnRBdmdNYXhMUlxcLCBcXGRUb25hbEVDTUEwNUV4TWF4TFJcXClcbmRWYXIgPC0gXFxkQW5ub3lNZWFuXFxcblxuc2VlZHMgPC0gYyg0MTA4NjUsIDI5NTQsIDcwODEyLCAyMDMsIDc5ODQpXG5cbmBgYFxuYGBgXG5cbjwhLS0gcm5iLXNvdXJjZS1lbmQgLS0+XG4ifQ== -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5pVmFycyA8LSBjKGRpZmZWYXIsIGV2ZW50VmFyLCBcXGRUb25hbEVDTUFBdmdNYXhMUlxcLCBcXGRUb25hbFNITUludDA1RXhNYXhMUlxcLCBcXGRUb25hbFNITUludEF2Z01heExSXFwsIFxcZFRvbmFsRUNNQTA1RXhNYXhMUlxcKVxuZFZhciA8LSBcXGRBbm5veU1lYW5cXFxuXG5zZWVkcyA8LSBjKDQxMDg2NSwgMjk1NCwgNzA4MTIsIDIwMywgNzk4NClcblxuYGBgXG5gYGAifQ== -->

```r
```r

iVars <- c(diffVar, eventVar, \dTonalECMAAvgMaxLR\, \dTonalSHMInt05ExMaxLR\, \dTonalSHMIntAvgMaxLR\, \dTonalECMA05ExMaxLR\)
dVar <- \dAnnoyMean\

seeds <- c(410865, 2954, 70812, 203, 7984)

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

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<!-- rnb-text-begin -->


###### Hyperparameter tuning


<!-- rnb-text-end -->


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<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVYRzV3SUR3dElHMTBjbmxVZFc1bEtHUmhkR0ZKYmoxemRHbHRSR0YwWVU1MWJTd2dhVlpoY25NOWFWWmhjbk1zSUdSV1lYSTlaRlpoY2l3Z2MyVmxaRDF6WldWa2Mxc3hPakpkTEZ4dUlDQWdJQ0FnSUNBZ0lDQWdJRzUwY21WbGN6MXVkSEpsWlhNc0lHMXBibk53YkdsMFBXMXBibk53YkdsMExDQnRhVzVpZFdOclpYUTliV2x1WW5WamEyVjBLVnh1Y0Z4dVhHNWdZR0FpZlE9PSAtLT5cblxuYGBgclxuXG5wIDwtIG10cnlUdW5lKGRhdGFJbj1zdGltRGF0YU51bSwgaVZhcnM9aVZhcnMsIGRWYXI9ZFZhciwgc2VlZD1zZWVkc1sxOjJdLFxuICAgICAgICAgICAgIG50cmVlcz1udHJlZXMsIG1pbnNwbGl0PW1pbnNwbGl0LCBtaW5idWNrZXQ9bWluYnVja2V0KVxucFxuXG5gYGBcblxuPCEtLSBybmItc291cmNlLWVuZCAtLT5cbiJ9 -->

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5wIDwtIG10cnlUdW5lKGRhdGFJbj1zdGltRGF0YU51bSwgaVZhcnM9aVZhcnMsIGRWYXI9ZFZhciwgc2VlZD1zZWVkc1sxOjJdLFxuICAgICAgICAgICAgIG50cmVlcz1udHJlZXMsIG1pbnNwbGl0PW1pbnNwbGl0LCBtaW5idWNrZXQ9bWluYnVja2V0KVxucFxuXG5gYGBcbmBgYCJ9 -->

```r
```r

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Selected hyperparameters


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVYRzV1ZEhKbFpTQThMU0F4TlRBeFhHNXRkSEo1SUR3dElHRnpMbWx1ZEdWblpYSW9iR1Z1WjNSb0tHbFdZWEp6S1M4eExqYzFLVnh1WEc1Z1lHQWlmUT09IC0tPlxuXG5gYGByXG5cbm50cmVlIDwtIDE1MDFcbm10cnkgPC0gYXMuaW50ZWdlcihsZW5ndGgoaVZhcnMpLzEuNzUpXG5cbmBgYFxuXG48IS0tIHJuYi1zb3VyY2UtZW5kIC0tPlxuIn0= -->

ntree <- 1501
mtry <- as.integer(length(iVars)/1.75)



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dVhHNXVkSEpsWlNBOExTQXhOVEF4WEc1dGRISjVJRHd0SUdGekxtbHVkR1ZuWlhJb2JHVnVaM1JvS0dsV1lYSnpLUzh4TGpjMUtWeHVYRzVnWUdCY2JtQmdZQ0o5IC0tPlxuXG5gYGByXG5gYGByXG5cbm50cmVlIDwtIDE1MDFcbm10cnkgPC0gYXMuaW50ZWdlcihsZW5ndGgoaVZhcnMpLzEuNzUpXG5cbmBgYFxuYGBgXG5cbjwhLS0gcm5iLXNvdXJjZS1lbmQgLS0+XG4ifQ== -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5udHJlZSA8LSAxNTAxXG5tdHJ5IDwtIGFzLmludGVnZXIobGVuZ3RoKGlWYXJzKS8xLjc1KVxuXG5gYGBcbmBgYCJ9 -->

```r
```r

ntree <- 1501
mtry <- as.integer(length(iVars)/1.75)

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


###### Run model

Train preliminary model


<!-- rnb-text-end -->


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<!-- rnb-output-begin 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 -->
# Tonality

nperm <- 5

resultsOutTonal2 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
                           ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutTonal2$OOB_RMSE
resultsOutTonal2$OOB_MAE
resultsOutTonal2$Rsquared



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


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```r
```r
# Tonality

nperm <- 5

resultsOutTonal2 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
                           ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutTonal2$OOB_RMSE
resultsOutTonal2$OOB_MAE
resultsOutTonal2$Rsquared

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Train multiple seeds model


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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# Tonality

resultsOutTonal2 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutTonal2$OOB_RMSE
resultsOutTonal2$OOB_MAE
resultsOutTonal2$Rsquared



<!-- rnb-output-end -->

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```r
```r
# Tonality

resultsOutTonal2 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutTonal2$OOB_RMSE
resultsOutTonal2$OOB_MAE
resultsOutTonal2$Rsquared

```
```

<!-- rnb-source-end -->


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# store results
resdAnnoyMnFitAB['Diff tonal no loud', 'RMSE'] <- resultsOutTonal2$OOB_RMSE
resdAnnoyMnFitAB['Diff tonal no loud', 'MAE'] <- resultsOutTonal2$OOB_MAE
resdAnnoyMnFitAB['Diff tonal no loud', 'Rsquared'] <- resultsOutTonal2$Rsquared
resdAnnoyMnPermImpAB$DiffTonal2 <- resultsOutTonal2$conditional_permimp



<!-- rnb-output-end -->

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```r
```r

# store results
resdAnnoyMnFitAB['Diff tonal no loud', 'RMSE'] <- resultsOutTonal2$OOB_RMSE
resdAnnoyMnFitAB['Diff tonal no loud', 'MAE'] <- resultsOutTonal2$OOB_MAE
resdAnnoyMnFitAB['Diff tonal no loud', 'Rsquared'] <- resultsOutTonal2$Rsquared
resdAnnoyMnPermImpAB$DiffTonal2 <- resultsOutTonal2$conditional_permimp

```
```

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###### Plot results


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par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutTonal2.conimp <- arrange(resultsOutTonal2$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutTonal2.conimp) + geom_col(aes(x=factor(rownames(resultsOutTonal2.conimp), levels=rownames(resultsOutTonal2.conimp)), y=CondPermImp), fill=mycolours[3], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (mean change in annoyance)\) + ggtitle(\dTonality w/o tonal loudness\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 2.2))
pBar

if (saveplots){
  ggsave(filename=\PtsABdAnnoyMndTonalConPermimp.svg\, width=8, height=2, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdAnnoyMndTonalConPermimp.svg\)
  
  ggsave(filename=\PtsABdAnnoyMndTonalConPermimp.pdf\, width=8, height=2, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdAnnoyMndTonalConPermimp.pdf\)
}



<!-- rnb-output-end -->

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```r
```r
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutTonal2.conimp <- arrange(resultsOutTonal2$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutTonal2.conimp) + geom_col(aes(x=factor(rownames(resultsOutTonal2.conimp), levels=rownames(resultsOutTonal2.conimp)), y=CondPermImp), fill=mycolours[3], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (mean change in annoyance)\) + ggtitle(\dTonality w/o tonal loudness\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 2.2))
pBar

if (saveplots){
  ggsave(filename=\PtsABdAnnoyMndTonalConPermimp.svg\, width=8, height=2, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdAnnoyMndTonalConPermimp.svg\)
  
  ggsave(filename=\PtsABdAnnoyMndTonalConPermimp.pdf\, width=8, height=2, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdAnnoyMndTonalConPermimp.pdf\)
}

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Selectec metric


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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dTonalVar <- \dTonalSHMIntAvgMaxLR\



<!-- rnb-output-end -->

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```r
```r

dTonalVar <- \dTonalSHMIntAvgMaxLR\

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


##### dFluctuation strength

###### Set variables


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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# Fluctuation strength
iVars <- c(diffVar, eventVar, \dFluctSHM10ExBin\, \dFluctSHM05ExBin\, \dFluctOV10ExMaxLR\, \dFluctOV05ExMaxLR\)
dVar <- \dAnnoyMean\

seeds <- c(418657, 84, 1630, 18659, 3687)



<!-- rnb-output-end -->

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```r
```r

# Fluctuation strength
iVars <- c(diffVar, eventVar, \dFluctSHM10ExBin\, \dFluctSHM05ExBin\, \dFluctOV10ExMaxLR\, \dFluctOV05ExMaxLR\)
dVar <- \dAnnoyMean\

seeds <- c(418657, 84, 1630, 18659, 3687)

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->



###### Hyperparameter tuning


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p



<!-- rnb-output-end -->

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```r
```r

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Selected hyperparameters


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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ntree <- 2501
mtry <- as.integer(length(iVars)/1.75)



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dVhHNXVkSEpsWlNBOExTQXlOVEF4WEc1dGRISjVJRHd0SUdGekxtbHVkR1ZuWlhJb2JHVnVaM1JvS0dsV1lYSnpLUzh4TGpjMUtWeHVYRzVnWUdCY2JtQmdZQ0o5IC0tPlxuXG5gYGByXG5gYGByXG5cbm50cmVlIDwtIDI1MDFcbm10cnkgPC0gYXMuaW50ZWdlcihsZW5ndGgoaVZhcnMpLzEuNzUpXG5cbmBgYFxuYGBgXG5cbjwhLS0gcm5iLXNvdXJjZS1lbmQgLS0+XG4ifQ== -->


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```r
```r

ntree <- 2501
mtry <- as.integer(length(iVars)/1.75)

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


###### Run model

Train preliminary model


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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nperm <- 5

resultsOutFluct <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutFluct$OOB_RMSE
resultsOutFluct$OOB_MAE
resultsOutFluct$Rsquared



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


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```r
```r

nperm <- 5

resultsOutFluct <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutFluct$OOB_RMSE
resultsOutFluct$OOB_MAE
resultsOutFluct$Rsquared

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->

Train multiple seeds model


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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resultsOutFluct <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutFluct$OOB_RMSE
resultsOutFluct$OOB_MAE
resultsOutFluct$Rsquared



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


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```r
```r

resultsOutFluct <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutFluct$OOB_RMSE
resultsOutFluct$OOB_MAE
resultsOutFluct$Rsquared

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->




<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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# store results
resdAnnoyMnFitAB['Diff fluct', 'RMSE'] <- resultsOutFluct$OOB_RMSE
resdAnnoyMnFitAB['Diff fluct', 'MAE'] <- resultsOutFluct$OOB_MAE
resdAnnoyMnFitAB['Diff fluct', 'Rsquared'] <- resultsOutFluct$Rsquared
resdAnnoyMnPermImpAB$DiffFluct <- resultsOutFluct$conditional_permimp



<!-- rnb-output-end -->

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```r
```r

# store results
resdAnnoyMnFitAB['Diff fluct', 'RMSE'] <- resultsOutFluct$OOB_RMSE
resdAnnoyMnFitAB['Diff fluct', 'MAE'] <- resultsOutFluct$OOB_MAE
resdAnnoyMnFitAB['Diff fluct', 'Rsquared'] <- resultsOutFluct$Rsquared
resdAnnoyMnPermImpAB$DiffFluct <- resultsOutFluct$conditional_permimp

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


###### Plot results


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutFluct.conimp <- arrange(resultsOutFluct$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutFluct.conimp) + geom_col(aes(x=factor(rownames(resultsOutFluct.conimp), levels=rownames(resultsOutFluct.conimp)), y=CondPermImp), fill=mycolours[4], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (mean change in annoyance)\) + ggtitle(\dFluctuation strength\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename=\PtsABdAnnoyMndFluctConPermimp.svg\, width=8, height=2, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdAnnoyMnFluctConPermimp.svg\)
  
  ggsave(filename=\PtsABdAnnoyMndFluctConPermimp.pdf\, width=8, height=2, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdAnnoyMnFluctConPermimp.pdf\)
}



<!-- rnb-output-end -->

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```r
```r
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutFluct.conimp <- arrange(resultsOutFluct$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutFluct.conimp) + geom_col(aes(x=factor(rownames(resultsOutFluct.conimp), levels=rownames(resultsOutFluct.conimp)), y=CondPermImp), fill=mycolours[4], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (mean change in annoyance)\) + ggtitle(\dFluctuation strength\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename=\PtsABdAnnoyMndFluctConPermimp.svg\, width=8, height=2, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdAnnoyMnFluctConPermimp.svg\)
  
  ggsave(filename=\PtsABdAnnoyMndFluctConPermimp.pdf\, width=8, height=2, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdAnnoyMnFluctConPermimp.pdf\)
}

```
```

<!-- rnb-source-end -->


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Selected metric


<!-- rnb-text-end -->


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dFluctVar <- \dFluctSHM05ExBin\



<!-- rnb-output-end -->

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```r
```r

dFluctVar <- \dFluctSHM05ExBin\

```
```

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<!-- rnb-output-end -->

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<!-- rnb-text-begin -->



##### dRoughness

###### Set variables


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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# Roughness
iVars <- c(diffVar, eventVar, \dRoughECMA10ExBin\, \dRoughECMA05ExBin\, \dRoughFZ10ExMaxLR\, \dRoughFZ05ExMaxLR\)
dVar <- \dAnnoyMean\

seeds <- c(69851, 85109, 410986, 1563, 896)



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG4jIFJvdWdobmVzc1xuaVZhcnMgPC0gYyhkaWZmVmFyLCBldmVudFZhciwgXFxkUm91Z2hFQ01BMTBFeEJpblxcLCBcXGRSb3VnaEVDTUEwNUV4QmluXFwsIFxcZFJvdWdoRloxMEV4TWF4TFJcXCwgXFxkUm91Z2hGWjA1RXhNYXhMUlxcKVxuZFZhciA8LSBcXGRBbm5veU1lYW5cXFxuXG5zZWVkcyA8LSBjKDY5ODUxLCA4NTEwOSwgNDEwOTg2LCAxNTYzLCA4OTYpXG5cbmBgYFxuYGBgIn0= -->

```r
```r

# Roughness
iVars <- c(diffVar, eventVar, \dRoughECMA10ExBin\, \dRoughECMA05ExBin\, \dRoughFZ10ExMaxLR\, \dRoughFZ05ExMaxLR\)
dVar <- \dAnnoyMean\

seeds <- c(69851, 85109, 410986, 1563, 896)

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


###### Hyperparameter tuning


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5wIDwtIG10cnlUdW5lKGRhdGFJbj1zdGltRGF0YU51bSwgaVZhcnM9aVZhcnMsIGRWYXI9ZFZhciwgc2VlZD1zZWVkc1sxOjJdLFxuICAgICAgICAgICAgIG50cmVlcz1udHJlZXMsIG1pbnNwbGl0PW1pbnNwbGl0LCBtaW5idWNrZXQ9bWluYnVja2V0KVxucFxuXG5gYGBcbmBgYCJ9 -->

```r
```r

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Selected hyperparameters


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVYRzV1ZEhKbFpTQThMU0ExTlRBeFhHNXRkSEo1SUR3dElHRnpMbWx1ZEdWblpYSW9iR1Z1WjNSb0tHbFdZWEp6S1M4eExqYzFLVnh1WEc1Z1lHQWlmUT09IC0tPlxuXG5gYGByXG5cbm50cmVlIDwtIDU1MDFcbm10cnkgPC0gYXMuaW50ZWdlcihsZW5ndGgoaVZhcnMpLzEuNzUpXG5cbmBgYFxuXG48IS0tIHJuYi1zb3VyY2UtZW5kIC0tPlxuIn0= -->

ntree <- 5501
mtry <- as.integer(length(iVars)/1.75)



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dVhHNXVkSEpsWlNBOExTQTFOVEF4WEc1dGRISjVJRHd0SUdGekxtbHVkR1ZuWlhJb2JHVnVaM1JvS0dsV1lYSnpLUzh4TGpjMUtWeHVYRzVnWUdCY2JtQmdZQ0o5IC0tPlxuXG5gYGByXG5gYGByXG5cbm50cmVlIDwtIDU1MDFcbm10cnkgPC0gYXMuaW50ZWdlcihsZW5ndGgoaVZhcnMpLzEuNzUpXG5cbmBgYFxuYGBgXG5cbjwhLS0gcm5iLXNvdXJjZS1lbmQgLS0+XG4ifQ== -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5udHJlZSA8LSA1NTAxXG5tdHJ5IDwtIGFzLmludGVnZXIobGVuZ3RoKGlWYXJzKS8xLjc1KVxuXG5gYGBcbmBgYCJ9 -->

```r
```r

ntree <- 5501
mtry <- as.integer(length(iVars)/1.75)

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


###### Run model

Train preliminary model


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->

nperm <- 5

resultsOutRough <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutRough$OOB_RMSE
resultsOutRough$OOB_MAE
resultsOutRough$Rsquared



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


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```r
```r

nperm <- 5

resultsOutRough <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutRough$OOB_RMSE
resultsOutRough$OOB_MAE
resultsOutRough$Rsquared

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Train multiple seeds model


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->

resultsOutRough <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutRough$OOB_RMSE
resultsOutRough$OOB_MAE
resultsOutRough$Rsquared



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


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```r
```r

resultsOutRough <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutRough$OOB_RMSE
resultsOutRough$OOB_MAE
resultsOutRough$Rsquared

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->



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<!-- rnb-output-begin 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 -->
# store results
resdAnnoyMnFitAB['Diff rough', 'RMSE'] <- resultsOutRough$OOB_RMSE
resdAnnoyMnFitAB['Diff rough', 'MAE'] <- resultsOutRough$OOB_MAE
resdAnnoyMnFitAB['Diff rough', 'Rsquared'] <- resultsOutRough$Rsquared
resdAnnoyMnPermImpAB$DiffRough <- resultsOutRough$conditional_permimp



<!-- rnb-output-end -->

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<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuIyBzdG9yZSByZXN1bHRzXG5yZXNkQW5ub3lNbkZpdEFCWydEaWZmIHJvdWdoJywgJ1JNU0UnXSA8LSByZXN1bHRzT3V0Um91Z2gkT09CX1JNU0VcbnJlc2RBbm5veU1uRml0QUJbJ0RpZmYgcm91Z2gnLCAnTUFFJ10gPC0gcmVzdWx0c091dFJvdWdoJE9PQl9NQUVcbnJlc2RBbm5veU1uRml0QUJbJ0RpZmYgcm91Z2gnLCAnUnNxdWFyZWQnXSA8LSByZXN1bHRzT3V0Um91Z2gkUnNxdWFyZWRcbnJlc2RBbm5veU1uUGVybUltcEFCJERpZmZSb3VnaCA8LSByZXN1bHRzT3V0Um91Z2gkY29uZGl0aW9uYWxfcGVybWltcFxuXG5gYGBcbmBgYCJ9 -->

```r
```r
# store results
resdAnnoyMnFitAB['Diff rough', 'RMSE'] <- resultsOutRough$OOB_RMSE
resdAnnoyMnFitAB['Diff rough', 'MAE'] <- resultsOutRough$OOB_MAE
resdAnnoyMnFitAB['Diff rough', 'Rsquared'] <- resultsOutRough$Rsquared
resdAnnoyMnPermImpAB$DiffRough <- resultsOutRough$conditional_permimp

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

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<!-- rnb-text-begin -->


###### Plot results


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<!-- rnb-chunk-begin -->


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par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutRough.conimp <- arrange(resultsOutRough$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutRough.conimp) + geom_col(aes(x=factor(rownames(resultsOutRough.conimp), levels=rownames(resultsOutRough.conimp)), y=CondPermImp), fill=mycolours[5], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (mean change in annoyance)\) + ggtitle(\dRoughness\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename=\PtsABdAnnoyMndRoughConPermimp.svg\, width=8, height=2, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdAnnoyMndRoughConPermimp.svg\)
  
  ggsave(filename=\PtsABdAnnoyMndRoughConPermimp.pdf\, width=8, height=2, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdAnnoyMndRoughConPermimp.pdf\)
}



<!-- rnb-output-end -->

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```r
```r
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutRough.conimp <- arrange(resultsOutRough$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutRough.conimp) + geom_col(aes(x=factor(rownames(resultsOutRough.conimp), levels=rownames(resultsOutRough.conimp)), y=CondPermImp), fill=mycolours[5], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (mean change in annoyance)\) + ggtitle(\dRoughness\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename=\PtsABdAnnoyMndRoughConPermimp.svg\, width=8, height=2, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdAnnoyMndRoughConPermimp.svg\)
  
  ggsave(filename=\PtsABdAnnoyMndRoughConPermimp.pdf\, width=8, height=2, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdAnnoyMndRoughConPermimp.pdf\)
}

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

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<!-- rnb-text-begin -->


Selected metric


<!-- rnb-text-end -->


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dRoughVar <- \dRoughECMA05ExBin\



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```r
```r

dRoughVar <- \dRoughECMA05ExBin\

```
```

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<!-- rnb-output-end -->

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<!-- rnb-text-begin -->


##### dImpulsiveness

###### Set variables


<!-- rnb-text-end -->


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# Impulsiveness
iVars <- c(diffVar, eventVar, \dImpulsSHMAvgMaxLR\, \dImpulsSHM05ExMaxLR\, \dImpulsSHMPowAvgMaxLR\)
dVar <- \dAnnoyMean\

seeds <- c(418659, 7805, 38475, 65834, 1653)



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```r
```r
# Impulsiveness
iVars <- c(diffVar, eventVar, \dImpulsSHMAvgMaxLR\, \dImpulsSHM05ExMaxLR\, \dImpulsSHMPowAvgMaxLR\)
dVar <- \dAnnoyMean\

seeds <- c(418659, 7805, 38475, 65834, 1653)

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

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<!-- rnb-text-begin -->


###### Hyperparameter tuning


<!-- rnb-text-end -->


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p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p



<!-- rnb-output-end -->

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```r
```r

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

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<!-- rnb-text-begin -->


Selected hyperparameters


<!-- rnb-text-end -->


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ntree <- 2501
mtry <- as.integer(length(iVars)/1.25)



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dVhHNXVkSEpsWlNBOExTQXlOVEF4WEc1dGRISjVJRHd0SUdGekxtbHVkR1ZuWlhJb2JHVnVaM1JvS0dsV1lYSnpLUzh4TGpJMUtWeHVYRzVnWUdCY2JtQmdZQ0o5IC0tPlxuXG5gYGByXG5gYGByXG5cbm50cmVlIDwtIDI1MDFcbm10cnkgPC0gYXMuaW50ZWdlcihsZW5ndGgoaVZhcnMpLzEuMjUpXG5cbmBgYFxuYGBgXG5cbjwhLS0gcm5iLXNvdXJjZS1lbmQgLS0+XG4ifQ== -->


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```r
```r

ntree <- 2501
mtry <- as.integer(length(iVars)/1.25)

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


###### Run model

Train preliminary model


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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nperm <- 5

resultsOutImpuls <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutImpuls$OOB_RMSE
resultsOutImpuls$OOB_MAE
resultsOutImpuls$Rsquared



<!-- rnb-output-end -->

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```r
```r

nperm <- 5

resultsOutImpuls <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutImpuls$OOB_RMSE
resultsOutImpuls$OOB_MAE
resultsOutImpuls$Rsquared

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Train multiple seeds model


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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resultsOutImpuls <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutImpuls$OOB_RMSE
resultsOutImpuls$OOB_MAE
resultsOutImpuls$Rsquared



<!-- rnb-output-end -->

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```r
```r

resultsOutImpuls <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutImpuls$OOB_RMSE
resultsOutImpuls$OOB_MAE
resultsOutImpuls$Rsquared

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->



<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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# store results
resdAnnoyMnFitAB['Diff impuls', 'RMSE'] <- resultsOutImpuls$OOB_RMSE
resdAnnoyMnFitAB['Diff impuls', 'MAE'] <- resultsOutImpuls$OOB_MAE
resdAnnoyMnFitAB['Diff impuls', 'Rsquared'] <- resultsOutImpuls$Rsquared
resdAnnoyMnPermImpAB$DiffImpuls <- resultsOutImpuls$conditional_permimp



<!-- rnb-output-end -->

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```r
```r

# store results
resdAnnoyMnFitAB['Diff impuls', 'RMSE'] <- resultsOutImpuls$OOB_RMSE
resdAnnoyMnFitAB['Diff impuls', 'MAE'] <- resultsOutImpuls$OOB_MAE
resdAnnoyMnFitAB['Diff impuls', 'Rsquared'] <- resultsOutImpuls$Rsquared
resdAnnoyMnPermImpAB$DiffImpuls <- resultsOutImpuls$conditional_permimp

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


###### Plot results


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutImpuls.conimp <- arrange(resultsOutImpuls$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutImpuls.conimp) + geom_col(aes(x=factor(rownames(resultsOutImpuls.conimp), levels=rownames(resultsOutImpuls.conimp)), y=CondPermImp), fill=mycolours[6], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (mean change in annoyance)\) + ggtitle(\dImpulsiveness\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename=\PtsABdAnnoyMndImpulsConPermimp.svg\, width=8, height=2, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdAnnoyMndImpulsConPermimp.svg\)
  
  ggsave(filename=\PtsABdAnnoyMndImpulsConPermimp.pdf\, width=8, height=2, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdAnnoyMndImpulsConPermimp.pdf\)
}



<!-- rnb-output-end -->

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```r
```r
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutImpuls.conimp <- arrange(resultsOutImpuls$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutImpuls.conimp) + geom_col(aes(x=factor(rownames(resultsOutImpuls.conimp), levels=rownames(resultsOutImpuls.conimp)), y=CondPermImp), fill=mycolours[6], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (mean change in annoyance)\) + ggtitle(\dImpulsiveness\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename=\PtsABdAnnoyMndImpulsConPermimp.svg\, width=8, height=2, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdAnnoyMndImpulsConPermimp.svg\)
  
  ggsave(filename=\PtsABdAnnoyMndImpulsConPermimp.pdf\, width=8, height=2, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdAnnoyMndImpulsConPermimp.pdf\)
}

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Selected metric


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVYRzVrU1cxd2RXeHpWbUZ5SUR3dElGd2laRWx0Y0hWc2MxTklUVEExUlhoTllYaE1VbHdpWEc1Y2JtQmdZQ0o5IC0tPlxuXG5gYGByXG5cbmRJbXB1bHNWYXIgPC0gXFxkSW1wdWxzU0hNMDVFeE1heExSXFxcblxuYGBgXG5cbjwhLS0gcm5iLXNvdXJjZS1lbmQgLS0+XG4ifQ== -->

dImpulsVar <- \dImpulsSHM05ExMaxLR\



<!-- rnb-output-end -->

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```r
```r

dImpulsVar <- \dImpulsSHM05ExMaxLR\

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


#### dSQM and loudness comparison

Now the highest importance dSQMs are ranked against each other, controlling for loudness difference.

##### Include dtonal loudness

###### Set variables


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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iVars <- c(diffVar, eventVar, dSharpVar, dTonLdVar, dFluctVar, dRoughVar, dImpulsVar)
dVar <- \dAnnoyMean\

seeds <- c(98465, 54163, 6541, 36485, 849675)



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5pVmFycyA8LSBjKGRpZmZWYXIsIGV2ZW50VmFyLCBkU2hhcnBWYXIsIGRUb25MZFZhciwgZEZsdWN0VmFyLCBkUm91Z2hWYXIsIGRJbXB1bHNWYXIpXG5kVmFyIDwtIFxcZEFubm95TWVhblxcXG5cbnNlZWRzIDwtIGMoOTg0NjUsIDU0MTYzLCA2NTQxLCAzNjQ4NSwgODQ5Njc1KVxuXG5gYGBcbmBgYCJ9 -->

```r
```r

iVars <- c(diffVar, eventVar, dSharpVar, dTonLdVar, dFluctVar, dRoughVar, dImpulsVar)
dVar <- \dAnnoyMean\

seeds <- c(98465, 54163, 6541, 36485, 849675)

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


###### Hyperparameter tuning


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5wIDwtIG10cnlUdW5lKGRhdGFJbj1zdGltRGF0YU51bSwgaVZhcnM9aVZhcnMsIGRWYXI9ZFZhciwgc2VlZD1zZWVkc1sxOjJdLFxuICAgICAgICAgICAgIG50cmVlcz1udHJlZXMsIG1pbnNwbGl0PW1pbnNwbGl0LCBtaW5idWNrZXQ9bWluYnVja2V0KVxucFxuXG5gYGBcbmBgYCJ9 -->

```r
```r

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Selected hyperparameters


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVYRzV1ZEhKbFpTQThMU0F5TlRGY2JtMTBjbmtnUEMwZ1lYTXVhVzUwWldkbGNpaHNaVzVuZEdnb2FWWmhjbk1wTHpFdU1qVXBYRzVjYm1CZ1lDSjkgLS0+XG5cbmBgYHJcblxubnRyZWUgPC0gMjUxXG5tdHJ5IDwtIGFzLmludGVnZXIobGVuZ3RoKGlWYXJzKS8xLjI1KVxuXG5gYGBcblxuPCEtLSBybmItc291cmNlLWVuZCAtLT5cbiJ9 -->

ntree <- 251
mtry <- as.integer(length(iVars)/1.25)



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dVhHNXVkSEpsWlNBOExTQXlOVEZjYm0xMGNua2dQQzBnWVhNdWFXNTBaV2RsY2loc1pXNW5kR2dvYVZaaGNuTXBMekV1TWpVcFhHNWNibUJnWUZ4dVlHQmdJbjA9IC0tPlxuXG5gYGByXG5gYGByXG5cbm50cmVlIDwtIDI1MVxubXRyeSA8LSBhcy5pbnRlZ2VyKGxlbmd0aChpVmFycykvMS4yNSlcblxuYGBgXG5gYGBcblxuPCEtLSBybmItc291cmNlLWVuZCAtLT5cbiJ9 -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5udHJlZSA8LSAyNTFcbm10cnkgPC0gYXMuaW50ZWdlcihsZW5ndGgoaVZhcnMpLzEuMjUpXG5cbmBgYFxuYGBgIn0= -->

```r
```r

ntree <- 251
mtry <- as.integer(length(iVars)/1.25)

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


###### Run model

Train preliminary model


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->

nperm <- 5

resultsOutSQMs1 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSQMs1$OOB_RMSE
resultsOutSQMs1$OOB_MAE
resultsOutSQMs1$Rsquared



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


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```r
```r

nperm <- 5

resultsOutSQMs1 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSQMs1$OOB_RMSE
resultsOutSQMs1$OOB_MAE
resultsOutSQMs1$Rsquared

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Train multiple seeds model


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->

resultsOutSQMs1 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSQMs1$OOB_RMSE
resultsOutSQMs1$OOB_MAE
resultsOutSQMs1$Rsquared



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


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```r
```r

resultsOutSQMs1 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSQMs1$OOB_RMSE
resultsOutSQMs1$OOB_MAE
resultsOutSQMs1$Rsquared

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->



<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->

# store results
resdAnnoyMnFitAB['Diff SQMs inc tonal loud', 'RMSE'] <- resultsOutSQMs1$OOB_RMSE
resdAnnoyMnFitAB['Diff SQMs inc tonal loud', 'MAE'] <- resultsOutSQMs1$OOB_MAE
resdAnnoyMnFitAB['Diff SQMs inc tonal loud', 'Rsquared'] <- resultsOutSQMs1$Rsquared
resdAnnoyMnPermImpAB$DiffSQMs1 <- resultsOutSQMs1$conditional_permimp



<!-- rnb-output-end -->

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<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG4jIHN0b3JlIHJlc3VsdHNcbnJlc2RBbm5veU1uRml0QUJbJ0RpZmYgU1FNcyBpbmMgdG9uYWwgbG91ZCcsICdSTVNFJ10gPC0gcmVzdWx0c091dFNRTXMxJE9PQl9STVNFXG5yZXNkQW5ub3lNbkZpdEFCWydEaWZmIFNRTXMgaW5jIHRvbmFsIGxvdWQnLCAnTUFFJ10gPC0gcmVzdWx0c091dFNRTXMxJE9PQl9NQUVcbnJlc2RBbm5veU1uRml0QUJbJ0RpZmYgU1FNcyBpbmMgdG9uYWwgbG91ZCcsICdSc3F1YXJlZCddIDwtIHJlc3VsdHNPdXRTUU1zMSRSc3F1YXJlZFxucmVzZEFubm95TW5QZXJtSW1wQUIkRGlmZlNRTXMxIDwtIHJlc3VsdHNPdXRTUU1zMSRjb25kaXRpb25hbF9wZXJtaW1wXG5cbmBgYFxuYGBgIn0= -->

```r
```r

# store results
resdAnnoyMnFitAB['Diff SQMs inc tonal loud', 'RMSE'] <- resultsOutSQMs1$OOB_RMSE
resdAnnoyMnFitAB['Diff SQMs inc tonal loud', 'MAE'] <- resultsOutSQMs1$OOB_MAE
resdAnnoyMnFitAB['Diff SQMs inc tonal loud', 'Rsquared'] <- resultsOutSQMs1$Rsquared
resdAnnoyMnPermImpAB$DiffSQMs1 <- resultsOutSQMs1$conditional_permimp

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

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<!-- rnb-text-begin -->


###### Plot results


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutSQMs1.conimp <- arrange(resultsOutSQMs1$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutSQMs1.conimp) + geom_col(aes(x=factor(rownames(resultsOutSQMs1.conimp), levels=rownames(resultsOutSQMs1.conimp)), y=CondPermImp), fill=mycolours[7], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (mean change in annoyance)\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 2.4))
pBar

if (saveplots){
  ggsave(filename=\PtsABdAnnoyMnDiffSQMsTonLdConPermimp.svg\, width=8, height=2.4, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdAnnoyMnDiffSQMsTonLdConPermimp.svg\)
  
  ggsave(filename=\PtsABdAnnoyMnDiffSQMsTonLdConPermimp.pdf\, width=8, height=2.4, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdAnnoyMnDiffSQMsTonLdConPermimp.pdf\)
}



<!-- rnb-output-end -->

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```r
```r
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutSQMs1.conimp <- arrange(resultsOutSQMs1$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutSQMs1.conimp) + geom_col(aes(x=factor(rownames(resultsOutSQMs1.conimp), levels=rownames(resultsOutSQMs1.conimp)), y=CondPermImp), fill=mycolours[7], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (mean change in annoyance)\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 2.4))
pBar

if (saveplots){
  ggsave(filename=\PtsABdAnnoyMnDiffSQMsTonLdConPermimp.svg\, width=8, height=2.4, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdAnnoyMnDiffSQMsTonLdConPermimp.svg\)
  
  ggsave(filename=\PtsABdAnnoyMnDiffSQMsTonLdConPermimp.pdf\, width=8, height=2.4, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdAnnoyMnDiffSQMsTonLdConPermimp.pdf\)
}

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


##### Exclude tonal loudness

###### Set variables


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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iVars <- c(diffVar, eventVar, dSharpVar, dTonalVar, dFluctVar, dRoughVar, dImpulsVar)
dVar <- \dAnnoyMean\

seeds <- c(49865, 7852, 845961, 410583, 36748)



<!-- rnb-output-end -->

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```r
```r

iVars <- c(diffVar, eventVar, dSharpVar, dTonalVar, dFluctVar, dRoughVar, dImpulsVar)
dVar <- \dAnnoyMean\

seeds <- c(49865, 7852, 845961, 410583, 36748)

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


###### Hyperparameter tuning


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p



<!-- rnb-output-end -->

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```r
```r

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Selected hyperparameters


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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ntree <- 5501
mtry <- as.integer(length(iVars)/1.25)



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dVhHNXVkSEpsWlNBOExTQTFOVEF4WEc1dGRISjVJRHd0SUdGekxtbHVkR1ZuWlhJb2JHVnVaM1JvS0dsV1lYSnpLUzh4TGpJMUtWeHVYRzVnWUdCY2JtQmdZQ0o5IC0tPlxuXG5gYGByXG5gYGByXG5cbm50cmVlIDwtIDU1MDFcbm10cnkgPC0gYXMuaW50ZWdlcihsZW5ndGgoaVZhcnMpLzEuMjUpXG5cbmBgYFxuYGBgXG5cbjwhLS0gcm5iLXNvdXJjZS1lbmQgLS0+XG4ifQ== -->


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```r
```r

ntree <- 5501
mtry <- as.integer(length(iVars)/1.25)

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->



###### Run model

Train preliminary model


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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nperm <- 5

resultsOutSQMs2 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSQMs2$OOB_RMSE
resultsOutSQMs2$OOB_MAE
resultsOutSQMs2$Rsquared



<!-- rnb-output-end -->

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```r
```r

nperm <- 5

resultsOutSQMs2 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSQMs2$OOB_RMSE
resultsOutSQMs2$OOB_MAE
resultsOutSQMs2$Rsquared

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Train multiple seeds model


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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resultsOutSQMs2 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSQMs2$OOB_RMSE
resultsOutSQMs2$OOB_MAE
resultsOutSQMs2$Rsquared



<!-- rnb-output-end -->

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```r
```r

resultsOutSQMs2 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSQMs2$OOB_RMSE
resultsOutSQMs2$OOB_MAE
resultsOutSQMs2$Rsquared

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->



<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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# store results
resdAnnoyMnFitAB['Diff SQMs no tonal loud', 'RMSE'] <- resultsOutSQMs2$OOB_RMSE
resdAnnoyMnFitAB['Diff SQMs no tonal loud', 'MAE'] <- resultsOutSQMs2$OOB_MAE
resdAnnoyMnFitAB['Diff SQMs no tonal loud', 'Rsquared'] <- resultsOutSQMs2$Rsquared
resdAnnoyMnPermImpAB$DiffSQMs2 <- resultsOutSQMs2$conditional_permimp



<!-- rnb-output-end -->

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```r
```r

# store results
resdAnnoyMnFitAB['Diff SQMs no tonal loud', 'RMSE'] <- resultsOutSQMs2$OOB_RMSE
resdAnnoyMnFitAB['Diff SQMs no tonal loud', 'MAE'] <- resultsOutSQMs2$OOB_MAE
resdAnnoyMnFitAB['Diff SQMs no tonal loud', 'Rsquared'] <- resultsOutSQMs2$Rsquared
resdAnnoyMnPermImpAB$DiffSQMs2 <- resultsOutSQMs2$conditional_permimp

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


###### Plot results


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutSQMs2.conimp <- arrange(resultsOutSQMs2$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutSQMs2.conimp) + geom_col(aes(x=factor(rownames(resultsOutSQMs2.conimp), levels=rownames(resultsOutSQMs2.conimp)), y=CondPermImp), fill=mycolours[7], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (mean change in annoyance)\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 2.4))
pBar

if (saveplots){
  ggsave(filename=\PtsABdAnnoyMnDiffSQMsNoTonLdConPermimp.svg\, width=8, height=2.4, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdAnnoyMnDiffSQMsNoTonLdConPermimp.svg\)
  
  ggsave(filename=\PtsABdAnnoyMnDiffSQMsNoTonLdConPermimp.pdf\, width=8, height=2.4, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdAnnoyMnDiffSQMsNoTonLdConPermimp.pdf\)
}



<!-- rnb-output-end -->

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```r
```r
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutSQMs2.conimp <- arrange(resultsOutSQMs2$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutSQMs2.conimp) + geom_col(aes(x=factor(rownames(resultsOutSQMs2.conimp), levels=rownames(resultsOutSQMs2.conimp)), y=CondPermImp), fill=mycolours[7], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (mean change in annoyance)\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 2.4))
pBar

if (saveplots){
  ggsave(filename=\PtsABdAnnoyMnDiffSQMsNoTonLdConPermimp.svg\, width=8, height=2.4, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdAnnoyMnDiffSQMsNoTonLdConPermimp.svg\)
  
  ggsave(filename=\PtsABdAnnoyMnDiffSQMsNoTonLdConPermimp.pdf\, width=8, height=2.4, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdAnnoyMnDiffSQMsNoTonLdConPermimp.pdf\)
}

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


### Save the results outputs to file


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->

if (savedata){
  utils::write.csv(resdAnnoyMnFitAB, paste(outDataPath, \\\PtsABCRFdAnnoyMnOOBFit.csv\, sep=\\))
  ii <- 0
  temp = list()
  for (res in resdAnnoyMnPermImpAB){
    ii <- ii + 1
    temp[[ii]] <- as.data.frame(resdAnnoyMnPermImpAB[ii])
    names(temp[[ii]]) <- names(resdAnnoyMnPermImpAB[ii])
  }
  openxlsx::write.xlsx(temp, paste(outDataPath, \\\PtsABCRFdAnnoyMnConPermimp.xlsx\,
                                   sep=\\),
                       rowNames=TRUE)
}



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


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```r
```r

if (savedata){
  utils::write.csv(resdAnnoyMnFitAB, paste(outDataPath, \\\PtsABCRFdAnnoyMnOOBFit.csv\, sep=\\))
  ii <- 0
  temp = list()
  for (res in resdAnnoyMnPermImpAB){
    ii <- ii + 1
    temp[[ii]] <- as.data.frame(resdAnnoyMnPermImpAB[ii])
    names(temp[[ii]]) <- names(resdAnnoyMnPermImpAB[ii])
  }
  openxlsx::write.xlsx(temp, paste(outDataPath, \\\PtsABCRFdAnnoyMnConPermimp.xlsx\,
                                   sep=\\),
                       rowNames=TRUE)
}

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


## (Change to) High annoyance

### Initialise results output variables


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->
resdHiAnnoyFitAB <- data.frame(RMSE = numeric(),
                             MAE = numeric(),
                             Rsquared = numeric())
resdHiAnnoyPermImpAB <- list()



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


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```r
```r
resdHiAnnoyFitAB <- data.frame(RMSE = numeric(),
                             MAE = numeric(),
                             Rsquared = numeric())
resdHiAnnoyPermImpAB <- list()

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


### All variables (absolute and difference)

#### Set variables


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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iVars <- names(stimDataNum)[which(names(stimDataNum) == 'UASEvents'):which(names(stimDataNum) == 'UASImpulsSHM05ExMaxLR')]
iVars <- iVars[! iVars %in% 'SNRlevel']
iVars <- c(iVars,
           names(stimDataNum)[which(colnames(stimDataNum)==\LAeqLAF90diff\):
                               which(colnames(stimDataNum)==\dImpulsSHM05ExMaxLR\)], \SNRlevel\)
dVar <- \dHighAnnoyPc\

seeds <- c(2, 312, 1897, 465978, 821659)



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


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```r
```r

iVars <- names(stimDataNum)[which(names(stimDataNum) == 'UASEvents'):which(names(stimDataNum) == 'UASImpulsSHM05ExMaxLR')]
iVars <- iVars[! iVars %in% 'SNRlevel']
iVars <- c(iVars,
           names(stimDataNum)[which(colnames(stimDataNum)==\LAeqLAF90diff\):
                               which(colnames(stimDataNum)==\dImpulsSHM05ExMaxLR\)], \SNRlevel\)
dVar <- \dHighAnnoyPc\

seeds <- c(2, 312, 1897, 465978, 821659)

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


#### Hyperparameter tuning


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
              ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)



<!-- rnb-output-end -->

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```r
```r

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
              ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
```
```

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-->

```

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p

```
```

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```

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" />

<!-- rnb-plot-end -->


<!-- rnb-output-end -->

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<!-- rnb-text-begin -->


Selected hyperparameters


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVYRzV1ZEhKbFpTQThMU0F4TlRBeFhHNXRkSEo1SUR3dElHRnpMbWx1ZEdWblpYSW9iR1Z1WjNSb0tHbFdZWEp6S1M4ekxqVXBYRzVjYm1CZ1lDSjkgLS0+XG5cbmBgYHJcblxubnRyZWUgPC0gMTUwMVxubXRyeSA8LSBhcy5pbnRlZ2VyKGxlbmd0aChpVmFycykvMy41KVxuXG5gYGBcblxuPCEtLSBybmItc291cmNlLWVuZCAtLT5cbiJ9 -->

ntree <- 1501
mtry <- as.integer(length(iVars)/3.5)



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dVhHNXVkSEpsWlNBOExTQXhOVEF4WEc1dGRISjVJRHd0SUdGekxtbHVkR1ZuWlhJb2JHVnVaM1JvS0dsV1lYSnpLUzh6TGpVcFhHNWNibUJnWUZ4dVlHQmdJbjA9IC0tPlxuXG5gYGByXG5gYGByXG5cbm50cmVlIDwtIDE1MDFcbm10cnkgPC0gYXMuaW50ZWdlcihsZW5ndGgoaVZhcnMpLzMuNSlcblxuYGBgXG5gYGBcblxuPCEtLSBybmItc291cmNlLWVuZCAtLT5cbiJ9 -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5udHJlZSA8LSAxNTAxXG5tdHJ5IDwtIGFzLmludGVnZXIobGVuZ3RoKGlWYXJzKS8zLjUpXG5cbmBgYFxuYGBgIn0= -->

```r
```r

ntree <- 1501
mtry <- as.integer(length(iVars)/3.5)

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


#### Run model

Train preliminary model


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->

nperm <- 5

resultsOutAbsDiffs <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutAbsDiffs$OOB_RMSE



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5ucGVybSA8LSA1XG5cbnJlc3VsdHNPdXRBYnNEaWZmcyA8LSBjcmZSZWcoZGF0YUluPXN0aW1EYXRhTnVtLCBpVmFycz1pVmFycywgZFZhcj1kVmFyLCBzZWVkcz1zZWVkc1sxOjJdLCBudHJlZT1udHJlZSwgbXRyeT1tdHJ5LCBwZXJtSW1wQ29uZFRocmVzPXBlcm1JbXBDb25kVGhyZXMsIG5wZXJtPW5wZXJtLCBtaW5zcGxpdD1taW5zcGxpdCwgbWluYnVja2V0PW1pbmJ1Y2tldClcblxuIyBwcmludCBtb2RlbCBwcmVkaWN0aW9uIHJlc3VsdHNcbnJlc3VsdHNPdXRBYnNEaWZmcyRPT0JfUk1TRVxuYGBgXG5gYGAifQ== -->

```r
```r

nperm <- 5

resultsOutAbsDiffs <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutAbsDiffs$OOB_RMSE
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDYuMzQyMjc2XG5cbjwhLS0gcm5iLXNvdXJjZS1iZWdpbiBleUprWVhSaElqb2lZR0JnY2x4dWNtVnpkV3gwYzA5MWRFRmljMFJwWm1aekpFOVBRbDlOUVVWY2JtQmdZQ0o5IC0tPlxuXG5gYGByXG5yZXN1bHRzT3V0QWJzRGlmZnMkT09CX01BRVxuYGBgXG5cbjwhLS0gcm5iLXNvdXJjZS1lbmQgLS0+XG4ifQ== -->

[1] 6.342276

resultsOutAbsDiffs$OOB_MAE



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dWNtVnpkV3gwYzA5MWRFRmljMFJwWm1aekpFOVBRbDlOUVVWY2JtQmdZRnh1WUdCZ0luMD0gLS0+XG5cbmBgYHJcbmBgYHJcbnJlc3VsdHNPdXRBYnNEaWZmcyRPT0JfTUFFXG5gYGBcbmBgYFxuXG48IS0tIHJuYi1zb3VyY2UtZW5kIC0tPlxuIn0= -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucmVzdWx0c091dEFic0RpZmZzJE9PQl9NQUVcbmBgYFxuYGBgIn0= -->

```r
```r
resultsOutAbsDiffs$OOB_MAE
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDQuODY1NDUyXG5cbjwhLS0gcm5iLXNvdXJjZS1iZWdpbiBleUprWVhSaElqb2lZR0JnY2x4dWNtVnpkV3gwYzA5MWRFRmljMFJwWm1aekpGSnpjWFZoY21Wa1hHNWdZR0FpZlE9PSAtLT5cblxuYGBgclxucmVzdWx0c091dEFic0RpZmZzJFJzcXVhcmVkXG5gYGBcblxuPCEtLSBybmItc291cmNlLWVuZCAtLT5cbiJ9 -->

[1] 4.865452

resultsOutAbsDiffs$Rsquared



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dWNtVnpkV3gwYzA5MWRFRmljMFJwWm1aekpGSnpjWFZoY21Wa1hHNWdZR0JjYm1CZ1lDSjkgLS0+XG5cbmBgYHJcbmBgYHJcbnJlc3VsdHNPdXRBYnNEaWZmcyRSc3F1YXJlZFxuYGBgXG5gYGBcblxuPCEtLSBybmItc291cmNlLWVuZCAtLT5cbiJ9 -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucmVzdWx0c091dEFic0RpZmZzJFJzcXVhcmVkXG5gYGBcbmBgYCJ9 -->

```r
```r
resultsOutAbsDiffs$Rsquared
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDAuNjUxNzY3NlxuIn0= -->

```
[1] 0.6517676
```



<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Train multiple seeds model


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->

resultsOutAbsDiffs <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutAbsDiffs$OOB_RMSE



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dVhHNXlaWE4xYkhSelQzVjBRV0p6UkdsbVpuTWdQQzBnYlhWc2RHbGZZM0ptVW1WbktHUmhkR0ZKYmoxemRHbHRSR0YwWVU1MWJTd2dhVlpoY25NOWFWWmhjbk1zSUdSV1lYSTlaRlpoY2l3Z2MyVmxaSE05YzJWbFpITXNJRzUwY21WbFBXNTBjbVZsTENCdGRISjVQVzEwY25rc0lIQmxjbTFKYlhCRGIyNWtWR2h5WlhNOWNHVnliVWx0Y0VOdmJtUlVhSEpsY3l3Z2JuQmxjbTA5Ym5CbGNtMHNJRzFwYm5Od2JHbDBQVzFwYm5Od2JHbDBMQ0J0YVc1aWRXTnJaWFE5YldsdVluVmphMlYwS1Z4dVhHNGpJSEJ5YVc1MElHMXZaR1ZzSUhCeVpXUnBZM1JwYjI0Z2NtVnpkV3gwYzF4dWNtVnpkV3gwYzA5MWRFRmljMFJwWm1aekpFOVBRbDlTVFZORlhHNWdZR0JjYm1CZ1lDSjkgLS0+XG5cbmBgYHJcbmBgYHJcblxucmVzdWx0c091dEFic0RpZmZzIDwtIG11bHRpX2NyZlJlZyhkYXRhSW49c3RpbURhdGFOdW0sIGlWYXJzPWlWYXJzLCBkVmFyPWRWYXIsIHNlZWRzPXNlZWRzLCBudHJlZT1udHJlZSwgbXRyeT1tdHJ5LCBwZXJtSW1wQ29uZFRocmVzPXBlcm1JbXBDb25kVGhyZXMsIG5wZXJtPW5wZXJtLCBtaW5zcGxpdD1taW5zcGxpdCwgbWluYnVja2V0PW1pbmJ1Y2tldClcblxuIyBwcmludCBtb2RlbCBwcmVkaWN0aW9uIHJlc3VsdHNcbnJlc3VsdHNPdXRBYnNEaWZmcyRPT0JfUk1TRVxuYGBgXG5gYGBcblxuPCEtLSBybmItc291cmNlLWVuZCAtLT5cbiJ9 -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5yZXN1bHRzT3V0QWJzRGlmZnMgPC0gbXVsdGlfY3JmUmVnKGRhdGFJbj1zdGltRGF0YU51bSwgaVZhcnM9aVZhcnMsIGRWYXI9ZFZhciwgc2VlZHM9c2VlZHMsIG50cmVlPW50cmVlLCBtdHJ5PW10cnksIHBlcm1JbXBDb25kVGhyZXM9cGVybUltcENvbmRUaHJlcywgbnBlcm09bnBlcm0sIG1pbnNwbGl0PW1pbnNwbGl0LCBtaW5idWNrZXQ9bWluYnVja2V0KVxuXG4jIHByaW50IG1vZGVsIHByZWRpY3Rpb24gcmVzdWx0c1xucmVzdWx0c091dEFic0RpZmZzJE9PQl9STVNFXG5gYGBcbmBgYCJ9 -->

```r
```r

resultsOutAbsDiffs <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutAbsDiffs$OOB_RMSE
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDYuMzM2MDM3XG5cbjwhLS0gcm5iLXNvdXJjZS1iZWdpbiBleUprWVhSaElqb2lZR0JnY2x4dWNtVnpkV3gwYzA5MWRFRmljMFJwWm1aekpFOVBRbDlOUVVWY2JtQmdZQ0o5IC0tPlxuXG5gYGByXG5yZXN1bHRzT3V0QWJzRGlmZnMkT09CX01BRVxuYGBgXG5cbjwhLS0gcm5iLXNvdXJjZS1lbmQgLS0+XG4ifQ== -->

[1] 6.336037

resultsOutAbsDiffs$OOB_MAE



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dWNtVnpkV3gwYzA5MWRFRmljMFJwWm1aekpFOVBRbDlOUVVWY2JtQmdZRnh1WUdCZ0luMD0gLS0+XG5cbmBgYHJcbmBgYHJcbnJlc3VsdHNPdXRBYnNEaWZmcyRPT0JfTUFFXG5gYGBcbmBgYFxuXG48IS0tIHJuYi1zb3VyY2UtZW5kIC0tPlxuIn0= -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucmVzdWx0c091dEFic0RpZmZzJE9PQl9NQUVcbmBgYFxuYGBgIn0= -->

```r
```r
resultsOutAbsDiffs$OOB_MAE
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDQuODUxNzAxXG5cbjwhLS0gcm5iLXNvdXJjZS1iZWdpbiBleUprWVhSaElqb2lZR0JnY2x4dWNtVnpkV3gwYzA5MWRFRmljMFJwWm1aekpGSnpjWFZoY21Wa1hHNWdZR0FpZlE9PSAtLT5cblxuYGBgclxucmVzdWx0c091dEFic0RpZmZzJFJzcXVhcmVkXG5gYGBcblxuPCEtLSBybmItc291cmNlLWVuZCAtLT5cbiJ9 -->

[1] 4.851701

resultsOutAbsDiffs$Rsquared



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dWNtVnpkV3gwYzA5MWRFRmljMFJwWm1aekpGSnpjWFZoY21Wa1hHNWdZR0JjYm1CZ1lDSjkgLS0+XG5cbmBgYHJcbmBgYHJcbnJlc3VsdHNPdXRBYnNEaWZmcyRSc3F1YXJlZFxuYGBgXG5gYGBcblxuPCEtLSBybmItc291cmNlLWVuZCAtLT5cbiJ9 -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucmVzdWx0c091dEFic0RpZmZzJFJzcXVhcmVkXG5gYGBcbmBgYCJ9 -->

```r
```r
resultsOutAbsDiffs$Rsquared
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDAuNjUyMzQ4NFxuIn0= -->

```
[1] 0.6523484
```



<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->



<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->
# store results
resdHiAnnoyFitAB['All vars', 'RMSE'] <- resultsOutAbsDiffs$OOB_RMSE
resdHiAnnoyFitAB['All vars', 'MAE'] <- resultsOutAbsDiffs$OOB_MAE
resdHiAnnoyFitAB['All vars', 'Rsquared'] <- resultsOutAbsDiffs$Rsquared
resdHiAnnoyPermImpAB$AllVars <- resultsOutAbsDiffs$conditional_permimp



<!-- rnb-output-end -->

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```r
```r
# store results
resdHiAnnoyFitAB['All vars', 'RMSE'] <- resultsOutAbsDiffs$OOB_RMSE
resdHiAnnoyFitAB['All vars', 'MAE'] <- resultsOutAbsDiffs$OOB_MAE
resdHiAnnoyFitAB['All vars', 'Rsquared'] <- resultsOutAbsDiffs$Rsquared
resdHiAnnoyPermImpAB$AllVars <- resultsOutAbsDiffs$conditional_permimp

```
```

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#### Plot results


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<!-- rnb-chunk-begin -->


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par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutAbsDiffs.conimp <- arrange(resultsOutAbsDiffs$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutAbsDiffs.conimp) + geom_col(aes(x=factor(rownames(resultsOutAbsDiffs.conimp), levels=rownames(resultsOutAbsDiffs.conimp)), y=CondPermImp), fill=mycolours[9], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (% highly annoyed)\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar



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```r
```r
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutAbsDiffs.conimp <- arrange(resultsOutAbsDiffs$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutAbsDiffs.conimp) + geom_col(aes(x=factor(rownames(resultsOutAbsDiffs.conimp), levels=rownames(resultsOutAbsDiffs.conimp)), y=CondPermImp), fill=mycolours[9], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (% highly annoyed)\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar
```
```

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```

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<img src=\data:image/png;base64
```



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\" 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}



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```r
```r

if (saveplots){
  ggsave(filename=\PtsABdHiAnnoyAllVarsConPermimp.svg\, width=8, height=20, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdHiAnnoyAllVarsConPermimp.svg\)

  ggsave(filename=\PtsABdHiAnnoyAllVarsConPermimp.pdf\, width=8, height=20, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdHiAnnoyAllVarsConPermimp.pdf\)
}

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->



<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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# Plot only positive values

resultsOutAbsDiffs.conimpPtv <- resultsOutAbsDiffs.conimp |>
                                          rownames_to_column('Metric') |>
                                                filter_if(is.numeric, all_vars(. > 0)) |>
                                                      column_to_rownames('Metric')

pBar <- ggplot(resultsOutAbsDiffs.conimpPtv) + geom_col(aes(x=factor(rownames(resultsOutAbsDiffs.conimpPtv), levels=rownames(resultsOutAbsDiffs.conimpPtv)), y=CondPermImp), fill=mycolours[9], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (% highly annoyed)\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar



<!-- rnb-output-end -->

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```r
```r

# Plot only positive values

resultsOutAbsDiffs.conimpPtv <- resultsOutAbsDiffs.conimp |>
                                          rownames_to_column('Metric') |>
                                                filter_if(is.numeric, all_vars(. > 0)) |>
                                                      column_to_rownames('Metric')

pBar <- ggplot(resultsOutAbsDiffs.conimpPtv) + geom_col(aes(x=factor(rownames(resultsOutAbsDiffs.conimpPtv), levels=rownames(resultsOutAbsDiffs.conimpPtv)), y=CondPermImp), fill=mycolours[9], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (% highly annoyed)\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

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```

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<img src=\data:image/png;base64
```



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grAAzXy/wk4EAZr5foWdCAI08/0KOxGEdJoBIiCdZkiaicAKmmU0XC1U4pjU8CZLHG/6pwPNYdVoltFwtUiJY64722CJ403+bKBZbESzuOFqoRLHhmZNlTjOK/lCOt+udJ7RcLVQieN4NGukxHHm37ICnj/kORHEWpqlG66GRUocM82aK3Fc1alp8gi+B7GKZhkNVydFShzbm2YdJY6dPrkIwmJlQmOp4eqoSIlj8xFKHO8wpJc4BkRAeoljQASkqwCACEinmdMTXwRhIZ1mSGiICKJymmUoofdZnQy1iMkiJT15cq6XHkvNvGxrmqbnV9iJIKofzTKUUGsT5jWzo3B+8oj8v+EnZvOXPP3flqbp+RV2IogaaRYroZZmvGZ2pHMX3NF6rMWmOWuYW9I0sax/WR/NEkqooZkRMY1I+Wj8pzEL7nTPZA1zS5omUD9qollaCTU002vMaMb6EmmjNJpxmndbmiZQP6qnWYYSqtVJs0bXaeEbIdfPps8OWMOEHdhb1JDQWFJCz+hpUc23eM21fXrGJP9vL9DbKdzus4aJEse+BiE9b+b0yUUQFqCZ71fYiSBAM9+vsBNBSKcZIALSaYaEhgg04NPUPVnh09xlNODT5J6s8GnuNJrwaVJPVvg0Vy9flNw/hE+TerLCp7nTQTTh09Qcg09zh4NowqdJb2PAp7nTQTTg0zQ9WeHT3GHApwk0APg0gQYgXQUAREA6zZye+CIIC+k0q0AB8PwKOxFE8zRLKJn8/BA1cGXMkp7OBYBmYoNonmaxkjlfEkepuWvS05kGaCY2iOZpxkpmwO1cJ6/v75HnhF7OiPq9pjydh5x3e/3epppmaW0Ry1qWjdOMlcyZbueqmDTpBtQiePKm7fea8nSqD0gCDfSrHTWNZkD9aJxmRv7kPpssiM5PxtTj1TbiTHk66YPTAWgmHo3TzAqkhmZBV9GMXjZK0izydNIHwcrRDBCBhmlmlMzv/9S0c20pxrHiafu9vpH0dJJK2qH3Zy+nmdMTXwRhISJvtmI0c/rkIggLCTSb7mvhFDQTG4QEmlmAZmKDkE4zQASk0wz5DBHYnGZFbZtR+dmLfuvavvViBrXUns31pwPNIcdoVtS2GZWfJVnJeDGnB2Q/qbz27KZ/NdAwitAsp23zsS0/SzSzXkyurld57dlCmtuL0podfJp2WcSnuYCitk1dfpbrtI+tF/PpIJweV197dpM/YwmeP+Q5EURemhWwbYa2/CyNZsaLyaNZWHnt2TxnJILnV9iJIHLQrKhtMyo/yzTTXkw9N6ug9qzTJxdBWORJaBSzbfJGZ/ZJ03gxR0N1I62i9iwgAhXnzaqxbV4C0EwsqqVZRbbNSwCaiYV0FQAQAek0c3riiyAspNMMCQ0RQZSh2akfmqbnV9iJIErQzBdN0/Mr7EQQhWk275OK6YOmiWX9y8I0C0hK8kLTBOpHYZopZj2le518TROoH2VGs6D9fOuaJiAChWnG9snta5pOT3wRhIV0TdPpk4sgLKRrmk6fXARhIV0FcPrkIggL6TQDREA6zZDMEAHQDGgA9duBs7q4UsOJqqXzfH830CgasANndXGtQTrf9C9eguezbyeCaMAOnNXFtQbpvLC0+6K0NAw7sF1u0Q6c1cW1Bul8kz8hE54PJE4E0YAdOKuLaw3SeZ6zkYLnV9iJIBqwA2d1cYV0vluo3w6c1cU1DGEH3ilIl84BEZAunQMiIF0FcHriiyAsQDPfr7ATQUinWQVak+dX2IkgitMsQ+O8b3uuJnD6YEHT1PO3yjXNAn+BgedX2IkgSoxmGRqnbYYZQ69MapqULAur1zSL/AVAU6iCZrHGqZkx71PSjBRN9gzPFjRNoln1mubCeiydWlZAs4TGaXUlxSaWmtgzvKhpEs2q1zSL/AVAUyhLs7TGqZmhbofTA6YZe4YXNc1oNAur1DSL/AVAUyhBswyNU8uQQUcrncO998kzfL6gacZzM5Q43pUgyiQ0ljTOM+65Sqv4Zsj9WFu3FzRN2nhcmabp9MlFEBbS82ZOn1wEYQGa+X6FnQhCOs0AEQDNgAYgnWZIaIhAjTQ7vc8VGs/TAlRWweMnVuacJxRO9V035MRvOtY0QDMRqI9mpGYehfOTjxNqOiXBMgoex6m0hMJ5xGuXY00D0rmIIOqimVYzj+Z3FdG0vqnGp+5E/ZdR8Dghc8YK55FJ3rINtE5N80VpzQ4+TbuswKeZC1rNJD1yfjLW+mYvDJ8fhFkFj2OZM6Fw8ncBWz9PdakEjGZig6iLZlrN1KOZphlp4urGGGYUPF4YzbTCqUazfk/tzP68RKxpgGYigqhxNAva50fqO82U4d77PaZZVsHj9NzMKJxq3+F6mgEiUBfN6Lb480P9imzP6pud2WH3koLHWuZMKZx0W52Q0/PssJOIFRAI6XkzQARAM6ABSKeZ0xNfBGEBmvl+hZ0IQjrNKhCaPL/CTgSRi2Ybl5812uScnibVM2TQYnQWDpdq7lrGp7lZ9JfB8yvsRBD5RrONy89y/ou1yzl99mw0vzOa93vpjVIVasv4NDeMHtgaCtJsXflZ3pDTsPNfK3YxzcKgqx2c7Y9b3UANWnyQ4dXBkEuhdYr7NKvzE2JZz7IYzdaWn+UNpwdEs7uztwZ2NLMOzoej2afmICwvsRpV3Ke5YfTA1lCAZpuUn02MZnfD6c1HI5qtdbSDc37n7NHxs+ggp4NHY3Y7Ffdplj4LQM3IR7ONy88m5mZ3FQ/3RvrdMXZwhsHNJzRh0wcJpz+5R0dShIJP09sgciY0Ni8/a540f9CnG+vDs0N6PuXqGurp8QOarJmDjGl0VIdtXT+ET9PbIKrPmxUpP/vlKKRRbw1AM7FBVE6zQuVnA+MAWA3QTGwQ0lUAQARAM6ABSKcZEhoikJtmWbrmcFmwpFewlx2ZV19LbueIpgnUj/yj2bKu+eDk/NbFwoNi0B5nOTJtcVp6098VTdPz2bcTQZSgWaxr8jrOiUXiJvmZMhyZ1nfJMpUjmuaL0podfJp2WZ1PM0vXpHWkV75pTJUTsiRlOTJnt7Tvkgybrmiang8kTgRRkGYLuiatY70yEjeJZhmOTBrNaJMp+4Ld0DQ9v8JOBJGfZhm6ph7NOklxk26aGY7MJM0q0TQBESiQ0FjWNbmSLM/NInHzeauX4cikJ03eZHb4ZjWaJiACbuTNimuagAi4QbPimiYgAm7QbDNAOhcbBGjm+xV2IgjpNENCQ0QQZWi2Xt6cRQ02qa0mT8Amrb3f/yaWL7WumdVdM0ft2ULRR/D8CjsRRKnRbL28aTVMo26+HExvjmeH3Vi+5D0zu2vmqD1bLHqgOVRDs0vlzajB5lXTak5XxIuKzuo9M7tr5qg9W95HiGW9y0podrm8aZocmkK06nb4kAeoSL7Ue2Z218xRe7ZY9EBzKE+z1fKmbbBpG2dqxkRFZ/Wemd01c9SeLRY90BxK0Wy9vGkabJq2mkS+m2O6Qdqis3rPzO6am9WedXriiyAsyiU01smb13WDTdNWk0euCb1ZdhhJnFrXzOyuuVHtWadPLoKwkJ43c/rkIggL0Mz3K+xEENJpBogAaAY0AOk0Qz5DBEAzoAHUSLPN27aOyknnJeP0fPbtRBD10SxH29aS0nnJQD2/wk4EURfNcrVtLSmdl5R2X5SWhmEHtkun27aWlM5Lhur5QOJEEHXRLFfb1pLSeeXBA1WjxtFs87atJaRzQATqolnetq1FpXNABKTnzQARkE4zpye+CMICNPP9CjsRBGjm+xV2IgjpNEPeTEQQ9dAswyh83zqDaY1Jzy6omEVLHFccPFA9ahrNMvq7Wmcwr2G1c7ygYhYtcVx18EDlqJtmsVHY0ozXsD6wqGIWLXFcWsvDsu5lzTRLGIUNzUxhY1I7F1XMoiWOqw4eqBx10ixtFDY002t4NAsXVMyiJY5Lxur57NuJIGqiWYZRWDuDzRpuP7GoYqJtq7dB1JXQWDIKn7Ez+IFec22f1M4lFRNtW30NQnrezOmTiyAspNMMEAHQDGgAoBnQAKTTDHkzEaiOZlTEuLO40uZks8yZlbVtLRm457NvJ4KojGYJ1TKBQMuRWebM6tq2lozc8yvsRBAV0cwWMQ7eab3T6k3eONw7Y3MmWUb2r3+aZc6srm1rSc3tRWnNDj5Nu6zZp6ltmbfODzuTzvRnVK6gy645RbOngy9HWebM6tq2lozd84HEiSAqopktYjzpUdFZxZrpgaWZmW0tmTOra9taNnigdlQ3mnER44hmQZfNmU9Oxs9GinIZ5ky0bd0hVEQzU8T4B2pO1WrtPeabI5kzJ63e54eXdXBF29adQS15s0l3/TYpoG2r56iDZnHua1MUb9vq9MQXQVhIVwGcPrkIwgI08/0KOxGEdJohbyYiiMpppivO9hZXT49NPdn25+oJcu8JT98CeoqET3MHUDXNdMXZSXtB3Zz36eGT3ZnPe2QUoLqyXNYMPs1dQLU0MxVnKe1K7cGiorOtjm7iqv1MlO8gmgUmaQufpvfLamkWV5zl1q1a1+Qv0xsj+wk7N4lmXHIWPs1dQLU0MxVnSYXk1q3MMM716+6aPJoNaaqVHs3g0/Q8iMpHM644S0vTunW495hoZWimvqMb6t303Aw+Td+DqJZmtuLsvE+OTKNrkmDZMU1cuaUrtS9hNXNonjTh0/Q9iAbyZpsJlhsANBMbRAM020yw3ACQzsVCugoAiABoBjQA6TRD3kwEytCM8q3t3w5IrlxA0oGpniU7vJ2uEkRfuRrQcgdN+DS9DaIEzRSXyH/5oL/8rmzKgUm+OvJpTm+Mwtlbgz+NqXthVgdN+DS9DaIwzVi+ZAKwXEm1yiav33j8Bje//ISrfZKgqdad3RrzdpxlpS/Te2FmB034NOHTXATLl+y/ZLlS3QFv97n5pfpAOzBJaVJ8ocGLt2ORSVFm2OqEmR004dP0NojCNGP5MuS6BawjsS8pZIemcWBa3VzTJehFo9n8TnYHTfg0vUWZ0SxoP+9p4fLpgIRITTP1gXZgakGT6cI+TXouoPlZGD4aZ3bQhE/TWxSmGcuV5L8cccPLoNVqv7c3UuMYmTHZgUmFMXpDepbs8HbmSXPIT46ZHTTh0/QVFefNcjs08wA0E4uq357N69DMA0jnYoOQrgI4fXIRhAVo5vsVdiII6TRD3kxEEOVplt07Uz1xni/UCOUHTUqKzXUeFz7N3UEFo1lW70wyZH6cMGuqFUbSnB6w2gSf5k6hSpolemeShWl+Evk0u5NW14gA5AX+4xg+zd1aVkizZO9MEiLnJ2PWmxSbwucHkaSpmPUBfJo7hqpottA7U49mmmZEHHV/tJLm7O3fDeDT3LEgKqBZVu9MMmRSsXaSNd/vMc0iSXNIt1X4NHcqiCoSGlm9M/nd2B4Ln3Tbmx12jaRpBCn4NHcqCOl5M6dPLoKwkE4zQARAM6ABgGZAA5BOM+TNRGAFzTLEyo2MmVndM+lxcqnbJvpp7kwQq0azDLFyI2NmVvfM+Z3RvN9bsRN8mls/wrZpFouVGxkzH2d0z+Ssf1cXpG1/3OoGai/004RPM8wUKzcyZmZ1z9SjmS1I+3A0+xT9NHcoiLU0S4uV4SbGzDCjeyZNzzq6IO38ztmj42fop7lLWEWzDLFysokxM6t7Jt00Q12QNgxuPqHpXSX9NEEzEViZ0FgSK0ebGDNHGd0zFWgKR3Mzxb4P1K3YHhb9NHcBZfNm1Rgz0U/Tc5SkWUXGTPTT9DwI6SqA0ycXQViAZr5fYSeCAM18v8JOBCGdZshniMAamuWRz01f1u5iTWN6TOBPapLON/5bga1h3WiWQz4PdV9WroyXrGnM9pIapfON/k5gq9iYZuvkcy2BW5qlahobmtUlnbtgd8Vy9XJTmq2Vz1kCt97edE3jeDSrRTq/NPYN4fns24kgNqLZRvI5SeB00+z3FmsaM83qk86LnhILz6+wE0Gso9nm8jlJ4GQDHvYWaxrbm2Yd0rnTJxdBWKxNaGwon/OYp98xu1ioaWxU9Xqkc6dPLoKwqCJvVl4+h3TuOaqooVFePi8unQMiIF0FAEQANAMagHSaIaEhIohqaXZ6PyFBxiAB1NYynrT2fv+bWL5kXXOeVfCY5NGQc8LpWNMAzUQEUSnNdHHjSaK0MWPOAqiuZTy9SX7OWL7UumZWweMjk2tbjDUN0ExEEBXSjBu5aglSa5emvnGrwwKorv75VHPIyJdGKs0seGzlUd65Tk3zRWnNDnZgu6y8besyuJGrliCtdnmq1UoWQLXe+ZAHqEi+NFJpVsFj/i5gxfRUJzvwhoZYVEgzbuSqJUjWLplhnOjXAiiPZpoxVr60umZWwWOWR3X92qegmXBUO5oF9CoQLTta9hzuPabnAUszqmWsSEgl27V8aXXNrILHLI+upRkgAhXSjKZhP9cS5APSLrm+MamVHRJAf8DFp3o0fnVmRr68+prRNTMLHpNSOiF/8dlhJxErIBB15802Uys3A6RzsUHUTbPN1MrNAJqJDUK6CuD0yUUQFqCZ71fYiSCk0wwJDRFokGbaeBlqf6ZWL+8nV5n6jkPO77b4VcnTwextrsDHP4JmYtHkaDYz7YJZx9TqZXoVNXt93guHPc6hWb/w9Nh4j0EzsdgGzVjH1Pn+9Nz9ntUAACAASURBVCpqj8iaEysCWiO9da7f1YBPU/JyCzQz/szI0plc1WGP1LHmGvd8bY8nXWM2wBsaYoPYAs20jpkazcwqGs2YfjyasUbKvV9BM+lBNE0zNc3SOqZWL9OrjvRmR1rfZI30Yg3NnD65CMKi2SfNFpXHYB3z+z8l9fLnyVXX9ul5kssPkb7J2/cmrdt9+0IuaCY2COl5M0AEQDOgAYBmQAMAzYAGIJ1mSGiICMIJn6aWMNUnT+DT9DMIJ3yaWsL8JNqIdoJP06cgnPBpalPToVU64dOET/NyFPdpsoT5pzF3GIZP00c44dPk0UytthvBp+kbnPBpmtfL9E0SPk0P4YpPU1FT17iFT9NHwKfp+0OeE0HAp+n7FXYiCOkqgNMnF0FYgGa+X2EngpBOMyQ0RKBxmq1za6YAn6YnaH40W+fWTAA+TV+wPZpd6tak1zeGrdfvwafpz3JrNLvUrfl0wFYm7pMIn6YnQWyNZpe6NYetVud0oGgGn6Y/QWyHZqvcmtTxOjCjGXyangSxjSfNlW5Nar3ZYTkUPk1/gnA2bxYsveoN6VwuXKXZdH+5SSdoJhau0iwLoJlYgGZAA5BOswpEAM9n304E0RTNZpm65ayC2rOlQ/P8CjsRRGOjWbZuWUHt2dKReX6FnQiicZqldcsKas+W1txelNbs4NO0ywZ8mquRrVtWUHu2/tCB0miaZgu6ZQW1Z+sPHSiNRmm2rFuWrj0LmolAg0+aGbpl+dqzgAhsNW+WoVuuAqRzsUFsk2ZZuuUqgGZig5CuAjh9chGEBWjm+xV2IgjpNANEQDrNkNAQgW3RjEVzTl6wcDk7bP/2QVTf+BKAZmKxtdGM8//PbJHjoEflkaP6xonNjuLvQTOx2D7NWDXn6tvaTTe8Ohi2zw+vDri28aTVne5f/3RFiWNI55DOL4elmVbNw4lO+2tv8MvB08HkTa5t/PwgfDr4UqfX8CKQ2CC2S7OoyHEYaoGT7pmng0dj0ta56Oz0QLepiGNNAzQTEcTWaDZ/V7vNtWo+4c4A7A0Opz+5x3dTS7NnI/UljjUNp08ugrDYXkJjuq9rGrOW/vevmU46NLI91K+ecW3j2WH380PTngd5M7GQnjcDRAA0AxoAaAY0AOk0c3riiyAspNMMCQ0RQZSnWYY6yc1b2+e30n01+dGS+ujM+7TDcqPWqJdrfFz9cncvGWsaoJmIICoYzbLUSTL2fpxo30o2khujcPYWJcLC6b3MRq1RL9fEccNEH1jQTGwQVdIsoU6SFW5+EnVu7U5a3YDsSUGX0rJ/HGc2ajW9XAMyodDPEc2oM1h9miaW9S8rpFlaneyEbLEkI5y65z0/CId066OEf3f2QXajVtvLNZz0+GdNM+4Dy8AbGmJRFc0W1Ek9mmmaEXHUTdKMZuHs7d8Nshu1ml6uNH3jn81oRi04E7GmAZqJQAU0y1In6S0x3XJ1uPd+j2lGDwg0PwuH7XF2o1bTy/XhaNLjn9kqPDvSBA2RNxOMKhIaS+rk2aF+juzZUsUkTerNwlAPY5mNWrmX6977rdb3f2oat1KjHeoDO4hjTcPpiS+CsJCeN3P65CIIC9DM9yvsRBCgme9X2IkgpNMMEAHpNENCQwSK0uz0QT9Km0awtRe1rNm+6Leu7Wuhkh4nl7ZPd2ndmqYJ1I+CNDOuygUEWnyMTJfqfytUzu+M5v2FOlMLXVq3pWkC9aMQzUzx4XHwTuudVm/yxuHeGauXlJDdv/6pNV0yzYxQqWhGgmafSui1P251AzVosQZqbJlqrNuWpvmitGYHn6ZdVurT5OLDVOO6M+lMf6Zuc0GXZSVFMzZVGlmTqvxboVKPZkEv5E0fjmaf2rrGJDbx3tvSND1/yHMiiEI04+LDigWT3qQ7PVYcmR5Ympm5la4qS6OZESppetahHacH8ztnj46fRRoo2TJJPhhvS9P0/Ao7EUTR0SygtxYjmgVdVi+fnIzJVBmZLplmWqikm6bakV7QCIObT/pqVqY1ULZlTmjWVkjTdPrkIgiLQjSjG+LtfusHagbVau095pujrkfcI1PlSy1r6idNLVQq0ChHczPFvg/CZyOrgY7p5Y7pfqt1/bCIpgmIQAV5s0l3/TZroOZz87trtwLNxKI8zeJMV3EEqwubGYBmYiFdBQBEQDrNnJ74IggL6TRDQkNEEJvTLMOPOeAvpttlEkm1Uj1Sdng78/qsnsvBp7lTQeQYzTL8mPTlQX/5STOlVpIqRRsboyYZAEL4NHcriCI0i/2Y/IVrxNJ7/ZPXbzx+43DPbDFjmVOtO7s15u2stYnlTfg0d2pZgGYJPyZ/UaMUuZQmt/vtC9Y3zRakPym+jGk/2s4aNYlm8GnuFvLSLO3H5C9cvHPGOfyQ9U2zBcuckTYZ9JZGsxA+zV1BDppl+DH5C9OMhEhNM0UkvQXLnI+ZLno7Y9SM52bwae4K8iQ0lvyYI1IvR1wjNmi12u9RGX+WMm1v1lZvSM+SHd7OGjVZyIRPc6eCqDhvVoG+eTlAM7FBVEuzKvTNywGaiQ1Cugrg9MlFEBbSaQaIgHSaIaEhAvlolqVrDpcdmPRy9rJX8+prie0c0jSB+pFzNFvWNR+cnN+6WHjzNWiPs7ya1tlJhWgd0jSB+lGUZrGuyev4Jf9I3KQ6jRleTfWt7ZHpkqb5orRmB5+mXVbk08zSNWkdGTB1A0xSJBXNsryas1sXcY9MdzRNzx/ynAiiCM0WdE1axwbMSNwkmmV4NWk0M80LXdI0Pb/CTgSRk2YZuqYezTpJcZNumhlezQTNKtM0nT65CMIib0JjWddkeZLnZpG4+TzTq0lPmrZHZmWaJiAC0vNmgAiAZkADAM2ABiCdZk5PfBGEhXSaIaEhIogqejYtC50PjFLJOVwDVjEZE/0Qytkw2oWfNtUzJXya3gZRxWiW1VCTcl6pCsYmU0awUiaBd9G6Jnya/gZRKc0SDTVpFednqRgtddTkvD+VqR1ZKZP35V20EgCfpr/LKmmWbKhJq6YHQZuL0ZLTjlRMXaZWS5mmtruuP6t1Tfg0vUVlNFtoqKlHM1OMlqijRzO2cRopk7bUu5jRDD5Nb1EFzbIaauq52VwXow16Zm6mbZyRlHlsdtG6ZiFNEzQTgUoSGktCp30n1hSj5RsfqZhk4xyTlMkPkGoKxrt8/6eka/68UO1Zpye+CMKi2bxZORsnaCY2iEZpVtLGCZqJDUK6CuD0yUUQFtJpBogAaAY0AOk0Qz5DBCqiWc4uruSzq8gOXDZyoAlUQ7PcXVyrswOXjDz0fvbtRBCVqAD5u7hWZwde+LnA8kVpaRh2YLustG3rAgp0ca3ODlwqcobnA4kTQVRBswJdXKuzA5eKnOH5FXYiiIpGs7xdXFHieLdQyYtAubu47o1gB94pLNHsiyuvfPW9V0ocsb4qx6CZWCzS7Ou//PqX4dc/+qjwAWuscgyaicUizb7554pmX3znD9uKZxUgnYsNIuOmeeXKt4sPZnUCNBMbRPYjwL93kmdIaIgNIkWzr394RSPXcJbXDkyzt1llduAcgV4Cz6+wE0GkR7O/M3OyfKNZXjsw9QarzA6cJ1BgW9iCHThFs9J24IWfsXRyuUQzunF+N8yFnHbgJM3K24HzhQpsB0sJjV+8op4286Vn89qBEzQrbwcu8DcDjWMpPfsPaXqWb26W1w5saVaFHdjpiS+CsFi6af67X6kv/ypfQiOfHdi2dIUdeHeCyE5olNE0VwF24B0NIjOh8dU/Lv37MgE78K4GsXTT/DB3erYxQDoXi+U3NL755+HXv9xWOCsBmonFEs1+GX7x6jf/TMxohoSGCCzdND/79kcf5svPnhbs3LqgRsGn6THKv6FRtHOr0TNjwKfpbxDphMaPPtI5jc0fAdijWahzq9Ezh1cHQ06jdeDTLBWHLJ/m3+VTAdijWahzq9UzSWhivx18mv4GsfQIkNMGwB7NQp1brQR6OnjEwsAYPk1/g1gazVhnyjWaBbr8et7OrUbPDKc/uUc6qCIUfJreYmk0yzk3Y0Nmkc6tbxoJdEzvdkz3W63rhTRNQATKzs2qwZejcH537VagmVhkJjTq0jQvRWBqnK0GaCYW0jVNpye+CMJCuqbp9MlFEBbQNH2/wk4EUYGmuSkyvZmJVSaTOyHHpzF1ng5mb494024ImgkOoslK2lnezNSqo3B+MqaCtMbUqbX16fH0OBFrGpDORWAbNEt6M1OrjuZ3Qy5IyzYoU9KWbQNd+DRFL9PS+V+GdSLLm7mwqhNyQVouTctyaaDuol1jIcBoJhaLlpM6UxlZ3szUqiOdo3064NGM5VISrUAz8UjT7JcsNtXla8ryZqZWHfFm8zu6NC3LpRdraOb0xBdBWGTNzT6rZ0zLbtUar7q2T8+TAesBQ/MOUW9CVW3N+7OgmdggFkez8JtfuCoCgGZyg1i2A79a+nfVBdBMbBCLL2mX/kU1AtK5WDSZNysL0EwspNMMCQ0R2Ixm2eVlczTQpMfKpe3h09wZbDiaXVJedgErGmjO74zm/V56c/g0dyaIvDRLlZfN0UCTSucFJFO2zw/bH7e6gRq04NPcXZ9mNjLLy+ZpoKlHM/Jy0qYPR7NP4dPcoSDy0GypvGyOBpo0PetoL+f8ztmj42cj+DR3KIgNaZZdXjZHA026aYbayxkGN5/0u/Bp7hI2TWhklZfN0UBTgUY59nIqPqn77xw+zR1CBXmzChpowqfpOcrTrIoGmvBpeg7pKoDTE18EYQGa+X6FnQhCOs2Q0BARRJU0m2fInAxO6SbVyTDutDlp7f3+Hn09Qz9Nf4OodDRLVZZd+OgooU4mPp/eHM8OuyGZM9FP019UTTNbWZbet5i8fuN9Vj0pk6tWsTpJ7/rTD7fGnER7yg+YbM5EP01/lxXTLKos+3QwebPf1qrncdAeq1VWnZz01A9caTsMug958GJzJvpp+ovKRzNT8nPYanUm3VDLUfTmxoFRJ0mxpJ6aJ2MWOG1jaipZi36a3qIumtFLQ0s0I3VSK5ZEM12sVnGIZIT5nRH6afobRNVPmqaybJfudcO9J1b17M0O39Dq5D9p0Stn3UmrpwXOCb25QRO2Gfpp+huE9LyZ0ycXQViAZr5fYSeCkE4zQARAM6ABgGZAA5BOM+TNRKAMzTJMwgP+YmrFRtD1jOkd72v7VPKzRc0QuyybP9EfhVu0A3s++3YiiFKjWYZJmL486C++ts2ZVrYIz+mzZ7pOnhbMudQxbwQ7sLdBVEOz2CTMX6LmrZPXbzx+Q33PG7Lbaf7rfk/twlU/tWBOPs05S+WwA++6HTgbGSZh/jK9oZu33u63L9gszBtyCdD53dlbg2cjrmGsBXNT6vg3IezA/gZRnmZpkzB/iZq3kqrZNSVmzWh2N5zefKRHs5AFcy51zFI5pHNvUYpmGSZh/hI1b9U006OZmZvdVTzcM3MzFsy51DFL5bADe4tyCY0lk/CIrMCjqHnre/SKI/uCzZPmD/p0Y1UDINUwDvQ6hdt9lsphB/YVtefNKjALW4BmYlE3zaowC1tAOhcbhHQVwOmTiyAsQDPfr7ATQUinGfJmIoKog2an97lS4/lCcVp+LKWk2FxnbalCS3e+fU0TqB810IwSZNSA9eN2TDO1YnpjFM7eIjtKOL1HGTPdnHX7miZQPyqnmW62Si0L5yfnXPhYDUjdSavLSdagSyndP46ZZoGp6rhlTRPL+peV04ybrbIQya0wqWGhuuc9PwiHdOsjsaA7+4Dz/yELm9vXNIH6UTnNuNmqGc00zYg46v5oRrNw9vbvtMyUGM22qml6Pvt2Iog6RrOgfU61WMgETIWP3+8xzejVR5qfhUOaaqXmZiU0TadPLoKwqJxm9Gj480P9SlCPCx/TbY+q/vCTZmjkJy5uPDRPmiU0TadPLoKwkJ43c/rkIggL6TQDRAA0AxoAaAY0AOk0Q95MBCqhGQuPndTP7fHCNqf3I81SPXK+eTJmR+ewxV101rV0TcaaBvJmIoKoZjRLNWWlasZLLV1J6DSa5eytga4JOv3xg5PzWxd3N2jpmog1DdBMRBCV0SwMutaZ2TJypG7Syv1cWei0mqUmkBbEacsNWrrCp7l26a9PM4Idj8iZydWMmWa2SevpIEi2ZR32iIBcbzui2bqWrvxbMJqJDaIimnFTVuPMnFqacQ1jZokWOo1mGXT08Mc8ZJqtb+maiDUNPAKIQHU3zTA0zkxDs+mxrmHMsiYLnUaznO73aIdJpI9v0NI1ESsgEFU9aeqXYdmZydWMjS/T9HM1QqfWLOlJU41e7OgkWdPsvaqlaycRKyAQ7ufN4pauoJlYuE+zuKUrpHOxQbhPsxigmdggQDPfr7ATQUinGfJmIoIoSrOMurMPMrq2GtWJPr5/yKWBru1Hdsy0bgmfpscoPJpl1J1dVjJJu+SF/Vj9H9sxF3RL+DT9RXmaxXVn1aqA27RO3jjcO9Ni5sl4un/9U/sx08xIm+Gibgmfpr/L0jRL1J2d3dJtWn/G9WZZZVI0ezr4cqQ/5pIHcXfWRd0SPk1/UY5m6bqz1Gua+2ey4mRpZmZb/DGPZkbaXNIt4dP0N4jCNMuoO5ukWaDFzCcnY2oCYD/WNNPS5pJuCZ+mv0EUT2gs1Z19QqVlTZtWK2ZOWr3P1eBE+uWZfdLU0ubV1xZ0y+to2+pvELXkzXLXm411y1UAzcQGUQfN8tebjXXLVYB0LhbSVQBABEAzoAFIpxnyZiLQCM10gVmuSNuZ6o6anKmNDZ0kb5r07ZDSsdxuk3Jqs7dHvHc3RN5McBDNjGa6wKx2MJmOmib5amRQlje5Yu3zXjjsmZZOWlufHps2sKCZ2CAao9nLgfVj6o6aaZqxvMk1HrlSKNdx1ObOW6R3qsEMPk34NNdBF5g1o5nuqJmimZE3Sb4Muopmmmvs8GyPJ12Th8NoJjaIRkcz9mOajpopmml5k0czZiSPZmzu5AK29dIMqB/Nzc3I60tZW9NR0yiW/NXIm7MjvfGRrkrLVWwv1tAMEIHmnjTJqLk3ou+po+bZYfTwefWBFkav7dMn/EYut9s0oujtvn0HBDQTC+l5M0AEpNPM6YkvgrAAzXy/wk4EAZr5foWdCEI6zZA3ExFEFTQrUnr2XG/jQu1ZoH5UMpoVKT3L28xdqD0L1I+qaJa79KyVmbZfexbL+peVjmZ5Ss+epWi2zdqzQP2ohmYFSs8mR7Pt1p71fPbtRBCV3TTzlp41ambZ2rNOn1wEYVHRk2bu0rMzvU3p2rNOn1wEYeF+3mx17VmnTy6CsHCfZqtrzwIi4D7NYoBmYgGaAQ0ANAMagHSaIW8mIogGabaZKTgF2IE9CaLJ0WwzU3ACsAP7EkTDNNvEFHzIWdvX78EOnGsJO7DBZqbgpwN2zAW9puzAQP3Ywmi2xhQ8VB+fDhTNYAf2B03PzdabgqnXQGBGM9iBPUHTT5prTcH0GmMnEtxhB/YDzubNgt7SKkjnYoNwlWbT/eUyyaCZ2CBcpVkWQDOxQYBmvl9hJ4KQTjMkNESgJppldHW9n1Iuac1gYRe0bfUWdY1mK7q6xmsWdkHbVm9RO81SXV35o3iNbvIatWlF21Zvl3XTLNXVNaFc0poZN3k9jtq0bqltq+ezbyeCqJVmS11d6ZvEGt19M2rTirat3gZRF82yu7rGyiWv0TSL2rSibau3QdSW0Fjq6nqWUi51n1du8hq3aUXbVl+DkJ43A0QANAMaAGgGNADQDGgA0mmGhIaIICqn2en9hAoZg7Kwui/r5+oJcm/EfVkn9LTZMqVYtqZpen6FnQiiapqRdhmrkBHYlmn7ss7fHei+rJ+E1rxJ22xL0/T8CjsRRLU009ZKViHjAsc0IHWmf3W4N+JOhiEXZeR87SENV2R3mm9T03xRWrODT9Mum/Fp6lrGrELGBY75y/TGyH6izZjqNvqn8YTLOV4lwWB7miZQP6qlma5lzCpkXOCYy3uquRl9QqPZsBfq0Uzt8HSgzZvb1DSB+lH5aBa0z1mFjAscP6YZvKGZ+o5uqHf13CwqdXxQWNMERKBamplaxqRCPogKHHON4uHe2eF19Ulo6vtQX9ZAq54Kt/tFNU1ABBrIm8U1iksC0rnYIBqgWVyjuCRAM7FBSFcBnD65CMICNPP9CjsRhHSaIaEhAvlolmG/HPAXU7ZzaesWPSdShR9yZfILtUa1dEjTBOpHztEsw35JXx70l5ushlrD5MwYbzi9MQqpZysVOHNJ0wTqR1GaxWZL/sKSpe3b+ob6XndctTTTrkyiovrCxWcd0jSxrH9ZkGYJ+yV/YclS9229ULe9oMvssXrlmLchkYk1gDtuaZqez76dCKIIzdL2S/6itSTdt1X31KSOq3TT7Pdsr+Cl0Sx0Q9P0/Ao7EUROmmXYL/kL08z0baX3fLrccZU0zCHRTG+jnhpofhbPzSrQNJ0+uQjCIm9CY8l+OXpJX0iy7Ji+rRNuXRLysKS+vVBPmryNedLU/Vir0jSdPrkIwqKGvNkk86mzAkA6F4vqaRZnvqoGaCYW0lUAQARAM6ABSKcZEhoigthS7Vn9EDpO7wKfprdBbKv2LFs202ZO+DT9DWJbtWfZ5ORG7dkXpTU7+DTtsul+mutqz864AZgTtWeB+rGt2rM8mrlRexaoH9uqPastm+VrzwIisKXas9f22bJZvvYsIALS82ZOP18hCAvQzPcr7EQQoJnvV9iJIEAz36+wE0FIpxkSGiJQD82yJU0q2rjfHlNRjUVBM4RP02vUJZ0vS5rzO6N5v8dFaJ+NrPCU2gc+TW9RN81iSZMNTd35r/u9iGZuaJpY1r+smWYJSdOMZndnbw0MzRzRND2ffTsRRJ00S0ua5GGieqDh9OYjM5q5oWl6foWdCKImmmVImnTTDLluY7Cn3ZnH6Ke5K0HUldDIkjTVM6Raozjz0Oqb6Ke5I0FIz5sBIgCaAQ0ANAMaAGgGNADpNHN64osgLKTTDHkzEUFUTrMNVfMhrVrYEXZgb4OofjTbSDWfn5zfuljosAM7sL9B1Eizlao5bwbpfFeW9dFspWrOmzkinQP1oyaarVXNdX1tJ6RzoH7UUK1xI9U8QTPYgf1HDQmNTVRz6kzdrcIO7PTEF0FYSM+bOX1yEYQFaOb7FXYiCNDM9yvsRBDSaQaIgHSaIaEhAlXSbB61YF0Cd2llTFosWp7ej3q6mg9gB/YYlY5mnOn6se7BehSvPbKdwAif8FeqeGybbdqNYAf2F5XTjNuxXh1MWrxQg1FXfct9DSfUzpVbtaox7GpMM275StujxHGZ4+xOiWMmxvTg6WDSfX4Q0oLabfK34fRn3M6VW7UGXTW42X6t1Ob1dKA2gh3Y3yDqGM2G9HbZAb1S1iHSqG/1aMbtXKlVq7qBPh1EPV25F+f0AHbgbR9BEs3UrOuZ5hYtaDRT3/LcTLdzpTulWgTUCF33dI1pBjuwr0HU8aSpa/539YK/5S6t3M41MBu0fh71dOWWr7PDNw5R4thbNJg3K93OFTQTi+ZoVr6dK2gmFtJVAEAEpNPM6YkvgrCQTjMkNEQE0bSmaWdoDmmanl9hJ4JoXNPUvgCXNE3Pr7ATQTStaTLN3NI0sax/2bSmaazBLmmaQP1oXNMkmrmlaQL1o3FN04xmVWmagAg0rWmSd3Ncoabp9MQXQVhI1zSdPrkIwkK6pun0yUUQFtJVAKdPLoKwkE4zQASk0wwJDRGog2Zar2yfL/TM5EpBlBSb9+kt2i57Np9EOih8mh6jjsJTWq88+TjRAJh0zRujcPYWJcjC6T0tcpJnk7JjL7UMCp+mt6icZkavnN+lMsaH3Umsa1I+I+hSlb0/jlkWYM+mItPLwXybtWdflNbs4NO0y0Z8mgSrV3YUzcYT+sHomkO69VH9xu7sAxY5j9mzaaVNtG3d+hEkPWkavZJHM00zq2vq0Sycvf27gRY5Q/Js8mgWom2rA0eQRLNIrySaTY/D4d77Rtf88YDnZ+FQ3SrNC2jmhbPpAdq2OnAESTQzeiU/R/boh57VNflJMzSiE4mcga5Rq3C7X7T2LCAC0vNmgAiAZkADAM2ABiCdZk5PfBGEhXSaIaEhIojCNLusb2ZvcUPq/WXeq1WLN/stzv47pGl6foWdCKL4aJbRNzOhP0bgLprWJTC9OaZk68uBS5qm51fYiSAqoFncN1Prj2rMakdqZqvDNkzjeSJuqf8eOaVpYln/sjzNEn0ztf6oKBR0mVL8ZXpjpDP6lPpX3HrXNU0TqB8laZbum6n1RzVcTQ/0yEU2JvbH2dFsduuPZ65pmkD9KE6zjL6ZWn8M6C0MVjMfE/0imvGjwrBD/1WnaQIiUCKhkdU3k/THB31jxeypLbQN8wfR86Si4/ReWJ2m6fTEF0FY1Jo3+3LELYGrAmgmNohaaRZcVu6sGEAzsUFIVwGcPrkIwkI6zQARkE4zJDREoBGaaUmTJc/OdL89pklbyyiWWkiiZAfLl+0xN9w0tWpPB7O3R7x3NwTNBKOZ0UwLl/M7ijI9FjmfmaKztvQs66Ls7hyT0GnSarqO7fSY1PcQNBOMxmj2cmAq6M1/3e8t0Yx1UfZDsdDJIoF2fN4655Lb8GmuW+6WTzMTWrg0o9nd2VuDBZppXZTlSxY6WfJkx2eg7qJdUxkNb2iIDaLR0UxNsjqUsJ3efJSmmdZFeTQLSejk0Ywdn2z2BM2kB9Hc3Gx6oFsCEJUC/WrQ9Fh/Nbqo6SWgNuO5GTs+L9bQzOmTiyAsmnvSJPfmHvcEUKR5eHYYPXySCEq66LV9+oQbbrLkybLopHW7b18BQd5MLKTnzQARAM2ABgCaAQ1AOs2cnvgiCAvQzPcr7EQQ0mmGvJmIIErSLJcpWGvjn++31PawA+9UEGVHs3ymYNLGn/fm7zplBwbqR2U028gUrNUkakUHO/BOLauirwT5ZAAAHQNJREFU2WamYP6ENoQdeLdQCc02NgXzaEYltWEH3i2UpVk+UzBp43RDvYsSx7sVROmERg5T8HWqfGxeuYYdeKeCaCBvVpkpGDQTG0QDNKvMFAyaiQ1CugoAiABoBjQA6TRDQkMEStLsdDAkj2/0c+TvDU8fGLVy0tr7/W9ivTIy/nYWDqVNmYkD0QMp53zTsaYBmolAOZpNf/zg5PzWReI50hovSdtktXJ6czw77MZ6JWuf2kqXPhZ/kDjQUagtKouxpgHpXEQQZWjGfl0mFgmY47iWsendyvn9p1oWN3ql0T6NMZhlz/bHrW6ghjn+YHh1MGyTAbijaaaOwYdF29a1S2/twOzXZZqRgPmmuiWaWsZR71bFwoc8IkV6pdY+9WhmZc+Ho9mn1hFM1JzEewftCz4s/z6MZmKDKEMz9usyzVjAjGsZm96tPJppili90mif2hjMe83vnD06fhaJoqeDR2MWQWk06/d0T86noJnwIEqOZoHiA41mRsA0tYyPde9WrVbeHHPfaa1XGu1Tz7p4rzC4+YReRtMfhNOf3CNllOdm4XA9zQARKEMzVixZneS5mZEt6fGSPmEbL71rQROtQ61XXn3NGn9pusV7qYEufDayouiYbrIkgl63Vah6dNhOIlZAIFzMm10mgoJmYuEizS4TQUEzsXCRZpcB0rnYIEAz36+wE0FIpxkSGiKCKESzDHfmgrapN7t1cbmuGS6rmPBpehtEsdFs2Z25qG0STFLtEl1zWcWET9NblKRZ7M5c0Db5Pf+T8QpdM1xSMeHT9HZZjmYJd+aCtklmS6LZ5brmsooJn6a3KEGztDtzUds8bPWi0SxT11xWMeHT9BbFaJbhzgzT2iaZLTXNLtM1l1VM+DS9DaJgQmPJnTlKa5udoNVqP1+la37/p8sqJnyavgbhRt5sMysnaCY2CDdotpmVEzQTG4QbNNsMkM7FAjQDGoB0miGhIQKl65sty5vWoJn0WJo2rFqzTDVrTRxpS5omUD9Kj2ZZxWcp4ZWSxU05MwLZAFLNWuMjbUnT9Hz27UQQ1dEsUXyWVk1Ik5zuX/9UjUddLs44eYMMl0ma6WatNIoNW6/f25am+aK0Zgefpl3W1rY1q/gsrZoeBNR+9cuRohiXmv2Zuh0G3STN9C5PB9zJMOhtS9P0fCBxIohqaLZQfFaPZvOTMc20iDd6NOvSzTNBM73LsNXqqDsq0Ww7mqbnV9iJIErTLKv4rJ6bKZo9G6lBrWfmZjRkxaNZ1KyVbriBHs0KaJqACJRPaCzJm0+iV2Z7n6uBie96JHhOyHhJG50nm7XST6R3UlfXApomIAIN5s0m3VWfBkv9d5YAmolFczSLM2JZmO6v+lQDNBML6SqA0xNfBGEBmvl+hZ0IQjrNkNAQEUTlNMvusBlrmDNb6kdtEdBTJPpp7kAQ1Y9ml4icBF7DTTXH1EqTs2nop7kLqJFmCyJnaDVMakPHrTQDk8tFP03vl/XRbFHkjIyZlK7lVprcVBP9NHcBNdEsQ+S0GqZpERw+HcSjGfppeo7qaXaZyGk1TG6qye+dRXMz9NP0PYgaEhpZHTZjDfPaPr1IG/DT5dA8aaKfpu9BSM+bOX1yEYQFaOb7FXYiCOk0A0QANAMagHSaIaEhAvXSLO3I5Bq1l39u1M4hpWONr/N0MHt7xA+j3RA0E4yaR7OUmsn65orPWe183guHPePr1JtPj6fHiVjTgHQuIoiGaBbrm8E7rXdavbRumVQ7WYFifcA05TzXfYLh09xpn+ZqpNRM0jdnh51Jh0anpG6ZVDuDrqKZ5hoVpA3aY7beETCaiQ2iGZol9M1Jj8qGpnXLpNrJUjqPZtyUc34CmnkQRAM0i9RM1jc1zdK6ZVrtVAuem3FTzos1NANEoPYnzYQjU5s42++1FnTLBbWTXp8lXye365zo+rW9OFZAIKTnzQARAM2ABiCdZk5PfBGEBWjm+xV2IgjpNENCQ0QQoJnvV9iJIMrRLDbzLiFV1JiWp/cPjQROUuV8qQ4yShx7jJKjWaKasUmumm8Xihozt0gav2N9wst1kGEH9hcV0IysS2okYsHyKnU47E6WihprGdzQTOvkS3WQYQf2d1kFzaYHVKb4+QFXK6ZyePxtqqgxy+Da+Ru3a12ogww7sL+oZjSjMsXqNkkLoon6dqGoMcvgNJr1e1YnX6qDjBLH/gZRzdzsmebWM100m75dKGrMMvi5mr0Ne1YnX6qDDDuwv0FU86Q54zLFXb3gb9NFjcemhrH6+aJvu7Yu1EFG29ZtH8FZmq3B6qLGuQGaiQ2iTpqtLmqcH5DOxUK6CgCIAGgGNADpNEPeTATy0yyjhvFgqB4KOwub3bqImurs/f43sWCpFSaWPCdx5x2UON7+Edx6BFiuYfzg5PzWxd30VkF7rKsXT2+OKdcRCZa8l5Y8PzFH226JY8+vsBNBlKFZ7PHldfO+Gn6mf3W4R6ok5V11vc+nmkNGsDR76Xqgh1rb3HKJ4xelNTvYge2yQjtwRg1jXqeoE7yp7nn0ZsakRzRjDfMhD0+RYGl7tYbTY65zvP0Sx54PJE4EUZRm6RrGvE7Ra3owPSbx8rDVi0Yz/T6QFSzNXjyahVTnGCWOdwEFaJZRw1iPZuqHLtOMVElNMyKeYom6QRrB0uxlXkeb3xmVLHEMiECRhMZSDeMRK5g8NxvunR12glar/Vz7eFv8RgYpnSxYXn1N7zWmHQJ9mFIljgERkJ43A0RAOs2cnvgiCAvQzPcr7EQQoJnvV9iJIKTTDHkzEUGUpVmGwvlgyYKZcG2SjnmWKnwcHwk+TW9RejS7pEtr2oKZcG1aHZPAu8RHgk/TW1RHs4UurSkLZuzatDom76sNmzSKDVuv34NP099lZTRb7NKasmDGrk2tYyYLG5O5kyxQPfg0/UU1NMvo0pqyYCZcm0bHpC31LmTuVHdUohl8mr4GUZpml3VpTVkwE67NSMe0hY3Z3KlHM/g0fQ2ifEJjSeF80l+0YHZi1ybpmOnCx+fa3Ek2Tvg0fQ2iwbzZatdm0Ft7ANBMbBDN0Wy1a3O6v97TCelcLKSrAIAIgGZAA5BOM+TNRKBs4allSfN+QoWMQU3ndF/Wz9UTpHoapalYQE+R8GnuQBClpfMMSTNWISPM+/SYafqyUqqNip5xNg0+zV0IojKaJSRNViHJGXB+2J3oNFmH/ZvcyVBnNri2nsnlwqcZB1L8OL75NFPIkjRZhSTXJulLnP0/HUxvjLj4rPqENySasVkTPs1dCKISmi1ImqxCsmuTGcZOJu3f5NFsSNulRzP4ND1HWZplSZqsQlrX5nDvMdHK0Ex9RzfUu+m5GXyavqN0QmNJ0jzTKuSDvik521NbtDrk37x+qN2c1OtkqKvSduHT3Ak0kDf7chTO767fbD1AM7FogGbBZW2d8gLSudggpKsATp9cBGEBmvl+hZ0IQjrNkDcTEUS9NGNFsjPdp/qM9ERJIgHVqb1u9EuzSjs2teQ5HnJyV/s6Twezt0fm2RR5M8GoeTSb31Es6bGi+YwtAOGc69Ra/TLl2DSSZzjsGV+nNntOj0l3D0EzwaifZmHQnf+637M008KB1S/Tjk0SCViBYn1A9+C8da6bCsOnKXnZzGh2d/bWIE0zo1+mHJssXwZdRTPNNVKqgvaYrXcEjGZiUTfN+tQwQA1S05uP0jQz+mXSscmjGUvpPJpxD875Se0083z27UQQDdw0QxYBgj1rz9Q00/plyrFpuqXbTuncg/NiDc2cPrkIwqL2J809bnSuePLw7FC/KDukpuhav2RYx+a1fdqMX8VlXycLohNdwrYXx7rZ35UDnl9hJ4KQnjdz+uQiCAvpNANEADQDGgBoBjQA6TRD3kwE6qCZVjJTPy8a6sLT+5Etc7rfepP6iEV9OZd7bSZjTQN5MxFB1DKa6dy/+WF2FBUBjUF2TitrvjWg5ptxX86sXpuJWNMAzUQEURfNwoA6NA0mr9943DKOy9i5qdVKa8vUude4L2dWr034NNcuvfZpZsKOZpPb/fb4+YEZzWLnJquVVtYc9oiAe6OoL2dWr00GRjOxQdRDM1YydaqfhCNNs9i5yWqllTWDjtWkTF/OrF6biVjTwCOACNR20yST8CRBs+lx7Nx8n9TKcyNrTvd7tEPclzOr12YiVkAganrS1PV+Wu33qBPAG1rMnEfOzVSp2RH7OMOXUV/OcLnXpn5QBc3EQnreDBAB6TRzeuKLICxAM9+vsBNBgGa+X2EngpBOM+TNRAQBmgENoPaXtBMNWlkdv/xzYweeUGIDdmC/UPNolrL7cgHkFZ+zHXhMmjnswJ6hIZrFBZCDd1rvtHrpMsZJOzBr5rADe7ZshmZxAeTZYWfSodEpWcY4aQdmzbxRO7Dns28ngmiGZokCyKRzcv3sRBnjpB04JM0cdmDPgmiAZpHdl9VxTbN0GeO0HXh+ZwQ7sGdBNFB4KmrQqnu6tt9rLZQxXrAD02vZsAP7FYT0vBkgAqAZ0ABAM6ABgGZAA5BOMyQ0RARRBc1OjY03hdmtC+PmnbT2fv+buCerVpCWLb9o27rtI7hNM2vjTa8N2mPt5p3eHM8Ou3FPVpYvsyy/aNvqbxClacbKI1OCLSXcnpX9Iidj7X97qjlkzL9Gvsyy/KJta6k4/LYDs/Jo/ZXBm+qeR+9WTHpEM3bzPuThKerJquXLLMvvttq2AvWjNM1YeWRKsN1X9808bPWi0UybLK3518iXWZZftG31F1WMZmYaZu2+TwekWGqaEfEUS9QN0ph/jXyZZflF21Z/UZpmrDyyBslzM2rP2iEj8HMtRtLApJ5CbU3jq69peXO8bPlF21aPIT1v5vTzFYKwAM18v8JOBAGa+X6FnQgCNPP9CjsRhHSaIaEhAtXSbFHdjIsbnz7Iq3Cyn4mdKulY0wDNRKBSmi2rm9YgRyWNcyqcR6Ya32KsaYBmIlAhzbLUzai48dVBXoXzyCRv+Th1appY1r+skGZZ6qYpbswljXMqnPxd0L4wKmmINzQEB1EhzTLVTVvc2IqXmyucajTrk2TFx0nEmgZoJiKIakezJXXTFDfmksY5Fc6jMByup5nTJxdBWFRIswx187oubkyf5Fc4dXmgHqukiVg3+7tywPMr7EQQ0vNmgAiAZkADAM2ABgCaAQ1AOs2Q0BARRDmazRcaqyZgqseGrFVSTvb0/qGpIUs6pt0zFi3h09z2EZylma5f9mMrQkZrj2wqjPAJfyVV08iUutBsvKfdCz5Nb4OogGasR14dcMVPzpF11bec2J+8QbImaZVa1TQ004VmzZ63xtP965/SXvBplorDZ58mU2F68HQw6T4/CGmh+BXyt+H0Z+oGGHRZq0yomrbQrN4zoEKzX45oL/g0/UU1o9mw1eqo2yQtiCbqWz2acTdN0iq1qskypS00q/ecn4xpOkZ7wafpL6qZmz3T3KIFjUvqW56bKa6Qx1LdKbWqyTKlLTSr91Q0ezZSgxqPZvBp+opqnjTpzkZvK/KCv2Vtc0KyZGA2oEat6ucLXWj2+z+1L9P2Pj/kRq7XD+HT9Ba15s1sk/KKAOlcbBB10izOgVUD0ExsENJVAKdPLoKwAM18v8JOBCGdZkhoiEBFNCtSfnZRa9qWpgnUj2poVqT8bLSw2JamCdSPKmhWrPysWQyvDoZt6pnZ2ZamiWX9yypoVqz8bKxtviS5gKo1bkfT9Hz27UQQ1fQFKFJ+1i5OB4/GbG/alqbp+RV2IoiKRrMC5WftYvqTe9Rps6Cm6fTJRRAWVdCsUPnZ7//UeDTpljrdbxXUNJ0+uQjCwo282ZejcH537VaQzsXCDZoFlzkKUgDNxMINmm0G0EwsQDOgAUinGRIaIoKonGYsRQYtq1zODtu/vW8r0NIaFinbn+/rB1D009yNIKofzTir+swql0HPlJ0laIPmUTg/+YRLzKKf5q4EUSPNWLJkiliaaYPmkc5dPBqjnyZ8moVhaWaUy0nLykZWxNQi5eyDEP00dwY10SxWLum+aWhmDJp6NNM3SvTT3A1UT7P5u1pG15LlpKcrGITTY2vQ1LU2HnEld/TT3A3UkNCY7usqsixZ/v1rrb0zrkD7QK+5ts9VgdQ9E/00dwfS82ZOP18hCAvQzPcr7EQQoJnvV9iJIEAz36+wE0FIpxkSGiIAmgENoCKaFah1vCyVww7sLaoazQrUOl6SymEH9hZV0ixnreMlqRzSOaTzNShQ63hJKocd2NsgKh7N8tQ6XpLKYQf2NoiK52Z5ah0vSeWwA3sbRMVPmjlqHZ8v238hnfuKZvJm1dQ6Bs3EohGaVVTrGDQTC+kqACAC0mnm9MQXQVhIpxkSGiKCaIBmLEt2pvvtMZVkaWmlUuuX+qcwaRQeDykda4TQ08Hs7RFv1g1BM8FBNDGaze8oqvTm/S4Z60zS1eiXy0bh571w2DNCqBZJp8fT40SsaYBmIoJoiGZh0J3/ut+LaWb0y2WjMIlTxywdaPnz1rnuXY0Sx5KXDY5md2dvDSKaGf1y2SgcdBXNNNdI/gzaY9aqCHhDQywaoVmfOlOokWp681FEM6NfLhmFWVbn0Yzlz/kJaOYBmrpphlz1M9gbGaXS6JdZRmH7khrLnxdraAaIQDNPmnsjGtIUWR5qa/B9o2cy0kZhrlnFQihXTp7oMsm9ONY0nJ74IggL6Xkzp08ugrAAzXy/wk4EAZr5foWdCEI6zQARkE4zJDREoCzNLunXOraljS/66glSC5hXX0tsx6KlGyWOgfpRkmaX9muNShsT5RYEzMi96UaJY6B+lKLZin6tUWljolksYPIm1r2JEsfwaW6CFf1adWlj/WpPLGBe8CbGvXnsSIljzx/ynAiiFM1W9GsNbWljGs1iAZM3se7N0I0Sx55fYSeCKDuaXdavNSptzDSLBExLM/NCWfkSx06fXARhUYpml/dr7b3UpY31k2bUr7XDm1j3Jkoc7wyk580AEQDNgAYAmgENQDrNnJ74IggL0Mz3K+xEEPXSLMOiSbXPWtdX+DS5Qu3GPk2nTy6CsKh5NFu2aM5ZA73cpzkmoXNzn6bTJxdBWNRPs0WLplY6L/VpstC5uU8TSxHLZkazpEVT0+xSnyYLnZv7NAERqJtmyxZNQ7PLfJohCZ2b+zQBEWjgprlg0TQ0u9ynqfaBT9Mz1P6kuWjRHJCcOVvl06SHUPg0/QoCeTPfr7ATQYBmvl9hJ4IAzXy/wk4EIZ1mgAiAZkADqIFmWshM/WwccDFO70euzOl+682T88NI7+xkeDiTsQICUcdoplP/5ofZUWgVpRjk4bSq5lsDaygwemeWhzMRKyAQNdEsDOjl/8Hk9RuPW8ZwSXaB88PuhN//vzqIXJk69ZpQCLI8nJf6NF8s/Fxg+aLk/vBpxsu6+2kmYUezye1+e/z8wFCIrJyUz+f0PvmYtKo57BEB985immV5OBl40hQbRC00YyFTZ/rpnR5NM7ZyMs10T02jagadyEcXJkazMO3hTMS62d+VA55fYSeCqOumGYZkt4xpNj22Vs7h3vvcU9OomtP9XmTX1HpnloczEetmf1cOeH6FnQiinidNqupDhs33qMP5G1rLZCsn65Smp6Z2ZdKTJjfftHpnmOHh1E+qeAQQC+TNgAYAmgENADQDGoB0mjk98UUQFqCZ71fYiSAK04yVSm5JwjY3U2s2qhMbg9xv87592HyzT+/Oti/6+mG0Oy9Ze9bpk4sgLIqPZpxQfTYyHktdazauExuBPZo6H/bjwfTmmIrivRxwbo0TZCVrzzp9chGERQU0Y4+lrjXLdWJj8ZKGog6Xm+WNWQKg/x6Nte5pSu2VqT2LpYhleZppj6WtNdtJipf8ZXpjpMcsGr0Urd7tzj7QSgGLlyVrzwIiUJJmihQTO3kKerpObCxehkOqr32s1UldX/bWH8/e/t0gXBjNiteeBUSgOM3m7+rqxtpjqWvNcp3YWLx8TPSLaMaPCsMO/Rem52Ylas8CIlAioTHd1w+P5LFs/72uNUt1Yh9E4iXLlVRu9gfR86Si4/ReGPI4R+Jl2dqzTk98EYRFrXmzL0fh/O76zTYFaCY2iFppFqRe5S8N0ExsEFABfL/CTgQhimaAWPAFlEGzLDjx9IkgLFYHAZqVAoKw8JVmgCCAZkADAM2ABgCaAQ1AKs1Ml4rtgvtlbBVz+5Lo9oNYeTKE0swYh7eMT7YdQOJFhG0HsfpkCKWZLYOwVXBRmS0jeq1q20GsPhlCacbvRW47CC4qs+UYopdEtx3E6pMhlGZb/wds8HTbd25nRrNw5ckQSrOtT0c0zPndcgRbPxmm1ciKkyGUZrqyy5YRVPqyUzHYl0S3HsTqkyGVZoAogGZAAwDNgAYAmgENADQDGgBoBjSAnaHZZ1euvPLvfrWw8pt/+quv/4JXmkUKWeuilV/9+R8u22Q9lncreCCO5B98/cMrr6pv/tVH/OOffZQMM/VTDqhT878VDmkJu0KzD7/zh/Czby2etw/1miX6pT7NxNc//M4f1myygOiXXPLbNto3A4rvn73yhYrmC6Ja+M0vvv1R/KH5KUeYFmqXEtRfxI7Q7Kv/ms6YvhAJqH+ytLjkn6359JIj8mi2cpM0ol9SYJBYscs3/+yjkGn29V/qFV//KEEz81OOMKPjql2+/od/yLvbJdgRmn32HXPC6N6pzv3/foX++V+5wv9k1fJV+peb+Ezdhr79EV+cb35x5btqTPg3tOLrH/3Nt/7lX/yKP/zqz//6ynf5Ynx4hQ9OO6r9+Ufa8H/583995Tv/4cp3/uMPv/2RGhr4l9COGb8t5NvaV2aHP9gjmS3+5lv/s93XbP6Z+tb+2i++qyj/PXXT/PeGXeYP+ItfqcivfPvf8E/f/NN/yTF/j8a2xJGi41155Wu7wYfRqVF/y+K/y6LYMZqpMeir7/33ijl0Wf/sIzXIKQb8P8SVb/0q+dlnrygC6TGAxoP/wiv+9hfqsqsN+cOvvvfqN794VX3zhVr3iqajut6v0o//ndpQ/awu3Xdp9vcjIiz9Et4x47eFdI/6W7vD35oj2S1eCe2+f6s3V8z68BXza9V6/Rd+9Y/Nn6qOobahMPmX8086XnPzjY9kjse/6FWzAR9Xn5r44KWxYzSj8/bhK/rCq+81l4hO+ufoM2LNq+ZWo67m3/2BVxDj7Id002TaqKFA0cLcnj57hX+k7/WBFBXNN2ZofDXrt+mHkcQOfCS7RRjtqzfhMcb+WsOEb/4nRZVvmX8W+td9EXOcDvBZdF+NjxSdCPoj9QZ8XH1qQLO80HOzr/7FZ9+lc3cJzRKffaEGF0uzr/7s//gXIa8wl5y/j2lmbiyWHK+a7zNoFh114beF2TSzW4TRvkmamV9rmKDGqQ9f/cLQ3fxZNCrGv1wdWD9JJI5kTgT/IrMBHxc0Kwh60lQzWkW36Ox+oWZfv/j2v41olvzsQ3NHjPblFeaS8/eKZvpupK4tjxJ089N3tK//cpFmX/+Qfk8YHXXht4ULNNNHirbQM8BXI8byfN/+WqYW3zIXafbFK8mf1JZ6Mpc8kv6qf5HZgI+rT81HmJvlxodXaN5M94RX1fff/UydUrXqyqv6G14kPvsPV77133z7b8x1oWv4Ba34R7yR/vDf/oK21ntz0kDNofW0+YqeRqsDqcW/5u++9d/Sk8KrfJCPln/bHyiu7/y13eE/miOZLYjkZt9/ZAP/1q9C82vpSVM/Epqbpj6kOsz//T01s49++kM0oU8c6a/18fgXhWaDD/XzBa3Ck6Z7SCcSmjuSTsBm4L8o0v1dgidq+rb6SIsbFEiDXAbQrCpsi2bhV/8gczUnvn6ZWLGUNVzE4gZQAdyDmnB/p5pbTFVH+kJPEww+5OfSFVi7QRmAZkADAM2ABgCaAQ0ANAMaAGgGNID/HzexeFeyJKPnAAAAAElFTkSuQmCC\" 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/>

<!-- rnb-plot-end -->


<!-- rnb-output-end -->

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if (saveplots){
  ggsave(filename=\PtsABdHiAnnoyAllVarsConPermimpPtv.svg\, width=8, height=15, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdHiAnnoyAllVarsConPermimp.svg\)
  
  ggsave(filename=\PtsABdHiAnnoyAllVarsConPermimpPtv.pdf\, width=8, height=15, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdHiAnnoyAllVarsConPermimp.pdf\)
}



<!-- rnb-output-end -->

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```r
```r

if (saveplots){
  ggsave(filename=\PtsABdHiAnnoyAllVarsConPermimpPtv.svg\, width=8, height=15, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdHiAnnoyAllVarsConPermimp.svg\)
  
  ggsave(filename=\PtsABdHiAnnoyAllVarsConPermimpPtv.pdf\, width=8, height=15, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdHiAnnoyAllVarsConPermimp.pdf\)
}

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->




<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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# Plot only values within 1% of the maximum

resultsOutAbsDiffs.conimp1pc <- resultsOutAbsDiffs.conimp |>
                                          rownames_to_column('Metric') |>
                                                filter_if(is.numeric, all_vars(. > max(resultsOutAbsDiffs.conimp)/100)) |>
                                                      column_to_rownames('Metric')

pBar <- ggplot(resultsOutAbsDiffs.conimp1pc) + geom_col(aes(x=factor(rownames(resultsOutAbsDiffs.conimp1pc), levels=rownames(resultsOutAbsDiffs.conimp1pc)), y=CondPermImp), fill=mycolours[9], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (% highly annoyed)\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar



<!-- rnb-output-end -->

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```r
```r

# Plot only values within 1% of the maximum

resultsOutAbsDiffs.conimp1pc <- resultsOutAbsDiffs.conimp |>
                                          rownames_to_column('Metric') |>
                                                filter_if(is.numeric, all_vars(. > max(resultsOutAbsDiffs.conimp)/100)) |>
                                                      column_to_rownames('Metric')

pBar <- ggplot(resultsOutAbsDiffs.conimp1pc) + geom_col(aes(x=factor(rownames(resultsOutAbsDiffs.conimp1pc), levels=rownames(resultsOutAbsDiffs.conimp1pc)), y=CondPermImp), fill=mycolours[9], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (% highly annoyed)\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

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```

<!-- rnb-plot-begin eyJoZWlnaHQiOjcsIndpZHRoIjo4LCJzaXplX2JlaGF2aW9yIjoxLCJjb25kaXRpb25zIjpbXX0= -->

<img src=\data:image/png;base64
```



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\" 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/>

<!-- rnb-plot-end -->


<!-- rnb-output-end -->

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if (saveplots){
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  unlink(\PtsABdHiAnnoyAllVarsConPermimp1pc.svg\)
  
  ggsave(filename=\PtsABdHiAnnoyAllVarsConPermimp1pc.pdf\, width=8, height=7, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdHiAnnoyAllVarsConPermimp1pc.pdf\)
}



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


<!-- rnb-source-begin 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 -->

```r
```r

if (saveplots){
  ggsave(filename=\PtsABdHiAnnoyAllVarsConPermimp1pc.svg\, width=8, height=7, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdHiAnnoyAllVarsConPermimp1pc.svg\)
  
  ggsave(filename=\PtsABdHiAnnoyAllVarsConPermimp1pc.pdf\, width=8, height=7, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdHiAnnoyAllVarsConPermimp1pc.pdf\)
}

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->



### Absolute variables

#### Set variables


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->

iVars <- names(stimDataNum)[which(names(stimDataNum) == 'UASEvents'):which(names(stimDataNum) == 'UASImpulsSHM05ExMaxLR')]
iVars <- iVars[! iVars %in% c('SNRlevel', 'IntermitRatioC2MaxLR', 'IntermitRatioC3MaxLR', 'IntermitRatioC5MaxLR')]
dVar <- \dHighAnnoyPc\

seeds <- c(578312, 544, 84894, 54654, 153157)



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5pVmFycyA8LSBuYW1lcyhzdGltRGF0YU51bSlbd2hpY2gobmFtZXMoc3RpbURhdGFOdW0pID09ICdVQVNFdmVudHMnKTp3aGljaChuYW1lcyhzdGltRGF0YU51bSkgPT0gJ1VBU0ltcHVsc1NITTA1RXhNYXhMUicpXVxuaVZhcnMgPC0gaVZhcnNbISBpVmFycyAlaW4lIGMoJ1NOUmxldmVsJywgJ0ludGVybWl0UmF0aW9DMk1heExSJywgJ0ludGVybWl0UmF0aW9DM01heExSJywgJ0ludGVybWl0UmF0aW9DNU1heExSJyldXG5kVmFyIDwtIFxcZEhpZ2hBbm5veVBjXFxcblxuc2VlZHMgPC0gYyg1NzgzMTIsIDU0NCwgODQ4OTQsIDU0NjU0LCAxNTMxNTcpXG5cbmBgYFxuYGBgIn0= -->

```r
```r

iVars <- names(stimDataNum)[which(names(stimDataNum) == 'UASEvents'):which(names(stimDataNum) == 'UASImpulsSHM05ExMaxLR')]
iVars <- iVars[! iVars %in% c('SNRlevel', 'IntermitRatioC2MaxLR', 'IntermitRatioC3MaxLR', 'IntermitRatioC5MaxLR')]
dVar <- \dHighAnnoyPc\

seeds <- c(578312, 544, 84894, 54654, 153157)

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


#### Hyperparameter tuning


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

if (saveplots){
  ggsave(filename=\PtsABdHiAnnoyAbsVarsHyperTune.svg\, width=12, height=4, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdHiAnnoyAbsVarsHyperTune.svg\)

  ggsave(filename=\PtsABdHiAnnoyAbsVarsHyperTune.pdf\, width=12, height=4, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdHiAnnoyAbsVarsHyperTune.pdf\)
}



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


<!-- rnb-source-begin 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 -->

```r
```r

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

if (saveplots){
  ggsave(filename=\PtsABdHiAnnoyAbsVarsHyperTune.svg\, width=12, height=4, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdHiAnnoyAbsVarsHyperTune.svg\)

  ggsave(filename=\PtsABdHiAnnoyAbsVarsHyperTune.pdf\, width=12, height=4, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdHiAnnoyAbsVarsHyperTune.pdf\)
}

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Selected hyperparameters


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVYRzV1ZEhKbFpTQThMU0ExTURGY2JtMTBjbmtnUEMwZ1lYTXVhVzUwWldkbGNpaHNaVzVuZEdnb2FWWmhjbk1wTHpFdU56VXBYRzVjYm1CZ1lDSjkgLS0+XG5cbmBgYHJcblxubnRyZWUgPC0gNTAxXG5tdHJ5IDwtIGFzLmludGVnZXIobGVuZ3RoKGlWYXJzKS8xLjc1KVxuXG5gYGBcblxuPCEtLSBybmItc291cmNlLWVuZCAtLT5cbiJ9 -->

ntree <- 501
mtry <- as.integer(length(iVars)/1.75)



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dVhHNXVkSEpsWlNBOExTQTFNREZjYm0xMGNua2dQQzBnWVhNdWFXNTBaV2RsY2loc1pXNW5kR2dvYVZaaGNuTXBMekV1TnpVcFhHNWNibUJnWUZ4dVlHQmdJbjA9IC0tPlxuXG5gYGByXG5gYGByXG5cbm50cmVlIDwtIDUwMVxubXRyeSA8LSBhcy5pbnRlZ2VyKGxlbmd0aChpVmFycykvMS43NSlcblxuYGBgXG5gYGBcblxuPCEtLSBybmItc291cmNlLWVuZCAtLT5cbiJ9 -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5udHJlZSA8LSA1MDFcbm10cnkgPC0gYXMuaW50ZWdlcihsZW5ndGgoaVZhcnMpLzEuNzUpXG5cbmBgYFxuYGBgIn0= -->

```r
```r

ntree <- 501
mtry <- as.integer(length(iVars)/1.75)

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


#### Run model

Train preliminary model


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->

nperm <- 10

resultsOutAbs <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutAbs$OOB_RMSE
resultsOutAbs$OOB_MAE
resultsOutAbs$Rsquared



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


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```r
```r

nperm <- 10

resultsOutAbs <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutAbs$OOB_RMSE
resultsOutAbs$OOB_MAE
resultsOutAbs$Rsquared

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->

Train multiple seeds model


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->

resultsOutAbs <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutAbs$OOB_RMSE
resultsOutAbs$OOB_MAE
resultsOutAbs$Rsquared



<!-- rnb-output-end -->

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```r
```r

resultsOutAbs <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutAbs$OOB_RMSE
resultsOutAbs$OOB_MAE
resultsOutAbs$Rsquared

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->




<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->

# store results
resdHiAnnoyFitAB['Abs vars', 'RMSE'] <- resultsOutAbs$OOB_RMSE
resdHiAnnoyFitAB['Abs vars', 'MAE'] <- resultsOutAbs$OOB_MAE
resdHiAnnoyFitAB['Abs vars', 'Rsquared'] <- resultsOutAbs$Rsquared
resdHiAnnoyPermImpAB$AbsVars <- resultsOutAbs$conditional_permimp



<!-- rnb-output-end -->

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```r
```r

# store results
resdHiAnnoyFitAB['Abs vars', 'RMSE'] <- resultsOutAbs$OOB_RMSE
resdHiAnnoyFitAB['Abs vars', 'MAE'] <- resultsOutAbs$OOB_MAE
resdHiAnnoyFitAB['Abs vars', 'Rsquared'] <- resultsOutAbs$Rsquared
resdHiAnnoyPermImpAB$AbsVars <- resultsOutAbs$conditional_permimp

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


#### Plot results


<!-- rnb-text-end -->


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par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutAbs.conimp <- arrange(resultsOutAbs$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutAbs.conimp) + geom_col(aes(x=factor(rownames(resultsOutAbs.conimp), levels=rownames(resultsOutAbs.conimp)), y=CondPermImp), fill=mycolours[1], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (% highly annoyed)\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) +
  coord_flip()
pBar

if (saveplots){
  ggsave(filename=\PtsABdHiAnnoyAbsVarsConPermimp.svg\, width=8, height=11.5, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdHiAnnoyAbsVarsConPermimp.svg\)
  
  ggsave(filename=\PtsABdHiAnnoyAbsVarsConPermimp.pdf\, width=8, height=11.5, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdHiAnnoyAbsVarsConPermimp.pdf\)
}



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```r
```r
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutAbs.conimp <- arrange(resultsOutAbs$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutAbs.conimp) + geom_col(aes(x=factor(rownames(resultsOutAbs.conimp), levels=rownames(resultsOutAbs.conimp)), y=CondPermImp), fill=mycolours[1], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (% highly annoyed)\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) +
  coord_flip()
pBar

if (saveplots){
  ggsave(filename=\PtsABdHiAnnoyAbsVarsConPermimp.svg\, width=8, height=11.5, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdHiAnnoyAbsVarsConPermimp.svg\)
  
  ggsave(filename=\PtsABdHiAnnoyAbsVarsConPermimp.pdf\, width=8, height=11.5, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdHiAnnoyAbsVarsConPermimp.pdf\)
}
```
```

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<!-- rnb-output-end -->

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# Plot only positive values
resultsOutAbs.conimpPtv <- resultsOutAbs.conimp |>
                                          rownames_to_column('Metric') |>
                                                filter_if(is.numeric, all_vars(. > 0)) |>
                                                      column_to_rownames('Metric')

pBar <- ggplot(resultsOutAbs.conimpPtv,) + geom_col(aes(x=factor(rownames(resultsOutAbs.conimpPtv), levels=rownames(resultsOutAbs.conimpPtv)), y=CondPermImp), fill=mycolours[1], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (% highly annoyed)\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename=\PtsABdHiAnnoyAbsVarsConPermimpPtv.svg\, width=8, height=8, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdHiAnnoyAbsVarsConPermimpPtv.svg\)
  
  ggsave(filename=\PtsABdHiAnnoyAbsVarsConPermimpPtv.pdf\, width=8, height=8, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdHiAnnoyAbsVarsConPermimpPtv.pdf\)
}



<!-- rnb-output-end -->

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```r
```r

# Plot only positive values
resultsOutAbs.conimpPtv <- resultsOutAbs.conimp |>
                                          rownames_to_column('Metric') |>
                                                filter_if(is.numeric, all_vars(. > 0)) |>
                                                      column_to_rownames('Metric')

pBar <- ggplot(resultsOutAbs.conimpPtv,) + geom_col(aes(x=factor(rownames(resultsOutAbs.conimpPtv), levels=rownames(resultsOutAbs.conimpPtv)), y=CondPermImp), fill=mycolours[1], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (% highly annoyed)\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename=\PtsABdHiAnnoyAbsVarsConPermimpPtv.svg\, width=8, height=8, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdHiAnnoyAbsVarsConPermimpPtv.svg\)
  
  ggsave(filename=\PtsABdHiAnnoyAbsVarsConPermimpPtv.pdf\, width=8, height=8, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdHiAnnoyAbsVarsConPermimpPtv.pdf\)
}

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

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<!-- rnb-chunk-begin -->


<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVYRzRqSUZCc2IzUWdiMjVzZVNCMllXeDFaWE1nZDJsMGFHbHVJREVsSUc5bUlIUm9aU0J0WVhocGJYVnRYRzV5WlhOMWJIUnpUM1YwUVdKekxtTnZibWx0Y0RGd1l5QThMU0J5WlhOMWJIUnpUM1YwUVdKekxtTnZibWx0Y0NCOFBseHVJQ0FnSUNBZ0lDQWdJQ0FnSUNBZ0lDQWdJQ0FnSUNBZ0lDQWdJQ0FnSUNBZ0lDQWdJQ0FnSUNBZ2NtOTNibUZ0WlhOZmRHOWZZMjlzZFcxdUtDZE5aWFJ5YVdNbktTQjhQbHh1SUNBZ0lDQWdJQ0FnSUNBZ0lDQWdJQ0FnSUNBZ0lDQWdJQ0FnSUNBZ0lDQWdJQ0FnSUNBZ0lDQWdJQ0FnSUNBZ1ptbHNkR1Z5WDJsbUtHbHpMbTUxYldWeWFXTXNJR0ZzYkY5MllYSnpLQzRnUGlCdFlYZ29jbVZ6ZFd4MGMwOTFkRUZpY3k1amIyNXBiWEFwTHpFd01Da3BJSHcrWEc0Z0lDQWdJQ0FnSUNBZ0lDQWdJQ0FnSUNBZ0lDQWdJQ0FnSUNBZ0lDQWdJQ0FnSUNBZ0lDQWdJQ0FnSUNBZ0lDQWdJQ0FnSUNCamIyeDFiVzVmZEc5ZmNtOTNibUZ0WlhNb0owMWxkSEpwWXljcFhHNWNibkJDWVhJZ1BDMGdaMmR3Ykc5MEtISmxjM1ZzZEhOUGRYUkJZbk11WTI5dWFXMXdNWEJqTENrZ0t5Qm5aVzl0WDJOdmJDaGhaWE1vZUQxbVlXTjBiM0lvY205M2JtRnRaWE1vY21WemRXeDBjMDkxZEVGaWN5NWpiMjVwYlhBeGNHTXBMQ0JzWlhabGJITTljbTkzYm1GdFpYTW9jbVZ6ZFd4MGMwOTFkRUZpY3k1amIyNXBiWEF4Y0dNcEtTd2dlVDFEYjI1a1VHVnliVWx0Y0Nrc0lHWnBiR3c5YlhsamIyeHZkWEp6V3pGZExDQjNhV1IwYUQwd0xqVXBJQ3NnYkdGaWN5aDRQVndpVm1GeWFXRmliR1ZjSWl3Z2VUMWNJa052Ym1ScGRHbHZibUZzSUhaaGNtbGhZbXhsSUhCbGNtMTFkR0YwYVc5dUlHbHRjRzl5ZEdGdVkyVWdLQ1VnYUdsbmFHeDVJR0Z1Ym05NVpXUXBYQ0lwSUNzZ2RHaGxiV1VvZEdWNGRDQTlJR1ZzWlcxbGJuUmZkR1Y0ZENobVlXMXBiSGtnUFNCY0luTmxjbWxtWENJcExDQndZVzVsYkM1bmNtbGtQV1ZzWlcxbGJuUmZiR2x1WlNoamIyeHZjaUE5SUhKbllpZ3lNelVzSURJek5Td2dNak0xTENBeE1EQXNJRzFoZUVOdmJHOXlWbUZzZFdVZ1BTQXlOVFVwTENCc2FXNWxkMmxrZEdnZ1BTQXdMakkxTENCc2FXNWxkSGx3WlNBOUlESXBLU0FySUdOdmIzSmtYMlpzYVhBb0tWeHVjRUpoY2x4dVhHNXBaaUFvYzJGMlpYQnNiM1J6S1h0Y2JpQWdaMmR6WVhabEtHWnBiR1Z1WVcxbFBWd2lVSFJ6UVVKa1NHbEJibTV2ZVVGaWMxWmhjbk5EYjI1UVpYSnRhVzF3TVhCakxuTjJaMXdpTENCM2FXUjBhRDA0TENCb1pXbG5hSFE5TkN3Z2NHRjBhRDFtYVd4bExuQmhkR2dvYjNWMFJtbG5VR0YwYUN3Z1hDSnpkbWRjSWlrcFhHNGdJSFZ1YkdsdWF5aGNJbEIwYzBGQ1pFaHBRVzV1YjNsQlluTldZWEp6UTI5dVVHVnliV2x0Y0RGd1l5NXpkbWRjSWlsY2JpQWdYRzRnSUdkbmMyRjJaU2htYVd4bGJtRnRaVDFjSWxCMGMwRkNaRWhwUVc1dWIzbEJZbk5XWVhKelEyOXVVR1Z5YldsdGNERndZeTV3WkdaY0lpd2dkMmxrZEdnOU9Dd2dhR1ZwWjJoMFBUUXNJSEJoZEdnOVptbHNaUzV3WVhSb0tHOTFkRVpwWjFCaGRHZ3NJRndpY0dSbVhDSXBLVnh1SUNCMWJteHBibXNvWENKUWRITkJRbVJJYVVGdWJtOTVRV0p6Vm1GeWMwTnZibEJsY20xcGJYQXhjR011Y0dSbVhDSXBYRzU5WEc1Y2JtQmdZQ0o5IC0tPlxuXG5gYGByXG5cbiMgUGxvdCBvbmx5IHZhbHVlcyB3aXRoaW4gMSUgb2YgdGhlIG1heGltdW1cbnJlc3VsdHNPdXRBYnMuY29uaW1wMXBjIDwtIHJlc3VsdHNPdXRBYnMuY29uaW1wIHw+XG4gICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICByb3duYW1lc190b19jb2x1bW4oJ01ldHJpYycpIHw+XG4gICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICBmaWx0ZXJfaWYoaXMubnVtZXJpYywgYWxsX3ZhcnMoLiA+IG1heChyZXN1bHRzT3V0QWJzLmNvbmltcCkvMTAwKSkgfD5cbiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIGNvbHVtbl90b19yb3duYW1lcygnTWV0cmljJylcblxucEJhciA8LSBnZ3Bsb3QocmVzdWx0c091dEFicy5jb25pbXAxcGMsKSArIGdlb21fY29sKGFlcyh4PWZhY3Rvcihyb3duYW1lcyhyZXN1bHRzT3V0QWJzLmNvbmltcDFwYyksIGxldmVscz1yb3duYW1lcyhyZXN1bHRzT3V0QWJzLmNvbmltcDFwYykpLCB5PUNvbmRQZXJtSW1wKSwgZmlsbD1teWNvbG91cnNbMV0sIHdpZHRoPTAuNSkgKyBsYWJzKHg9XFxWYXJpYWJsZVxcLCB5PVxcQ29uZGl0aW9uYWwgdmFyaWFibGUgcGVybXV0YXRpb24gaW1wb3J0YW5jZSAoJSBoaWdobHkgYW5ub3llZClcXCkgKyB0aGVtZSh0ZXh0ID0gZWxlbWVudF90ZXh0KGZhbWlseSA9IFxcc2VyaWZcXCksIHBhbmVsLmdyaWQ9ZWxlbWVudF9saW5lKGNvbG9yID0gcmdiKDIzNSwgMjM1LCAyMzUsIDEwMCwgbWF4Q29sb3JWYWx1ZSA9IDI1NSksIGxpbmV3aWR0aCA9IDAuMjUsIGxpbmV0eXBlID0gMikpICsgY29vcmRfZmxpcCgpXG5wQmFyXG5cbmlmIChzYXZlcGxvdHMpe1xuICBnZ3NhdmUoZmlsZW5hbWU9XFxQdHNBQmRIaUFubm95QWJzVmFyc0NvblBlcm1pbXAxcGMuc3ZnXFwsIHdpZHRoPTgsIGhlaWdodD00LCBwYXRoPWZpbGUucGF0aChvdXRGaWdQYXRoLCBcXHN2Z1xcKSlcbiAgdW5saW5rKFxcUHRzQUJkSGlBbm5veUFic1ZhcnNDb25QZXJtaW1wMXBjLnN2Z1xcKVxuICBcbiAgZ2dzYXZlKGZpbGVuYW1lPVxcUHRzQUJkSGlBbm5veUFic1ZhcnNDb25QZXJtaW1wMXBjLnBkZlxcLCB3aWR0aD04LCBoZWlnaHQ9NCwgcGF0aD1maWxlLnBhdGgob3V0RmlnUGF0aCwgXFxwZGZcXCkpXG4gIHVubGluayhcXFB0c0FCZEhpQW5ub3lBYnNWYXJzQ29uUGVybWltcDFwYy5wZGZcXClcbn1cblxuYGBgXG5cbjwhLS0gcm5iLXNvdXJjZS1lbmQgLS0+XG4ifQ== -->

# Plot only values within 1% of the maximum
resultsOutAbs.conimp1pc <- resultsOutAbs.conimp |>
                                          rownames_to_column('Metric') |>
                                                filter_if(is.numeric, all_vars(. > max(resultsOutAbs.conimp)/100)) |>
                                                      column_to_rownames('Metric')

pBar <- ggplot(resultsOutAbs.conimp1pc,) + geom_col(aes(x=factor(rownames(resultsOutAbs.conimp1pc), levels=rownames(resultsOutAbs.conimp1pc)), y=CondPermImp), fill=mycolours[1], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (% highly annoyed)\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename=\PtsABdHiAnnoyAbsVarsConPermimp1pc.svg\, width=8, height=4, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdHiAnnoyAbsVarsConPermimp1pc.svg\)
  
  ggsave(filename=\PtsABdHiAnnoyAbsVarsConPermimp1pc.pdf\, width=8, height=4, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdHiAnnoyAbsVarsConPermimp1pc.pdf\)
}



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


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```r
```r

# Plot only values within 1% of the maximum
resultsOutAbs.conimp1pc <- resultsOutAbs.conimp |>
                                          rownames_to_column('Metric') |>
                                                filter_if(is.numeric, all_vars(. > max(resultsOutAbs.conimp)/100)) |>
                                                      column_to_rownames('Metric')

pBar <- ggplot(resultsOutAbs.conimp1pc,) + geom_col(aes(x=factor(rownames(resultsOutAbs.conimp1pc), levels=rownames(resultsOutAbs.conimp1pc)), y=CondPermImp), fill=mycolours[1], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (% highly annoyed)\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename=\PtsABdHiAnnoyAbsVarsConPermimp1pc.svg\, width=8, height=4, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdHiAnnoyAbsVarsConPermimp1pc.svg\)
  
  ggsave(filename=\PtsABdHiAnnoyAbsVarsConPermimp1pc.pdf\, width=8, height=4, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdHiAnnoyAbsVarsConPermimp1pc.pdf\)
}

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Selected metric


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVYRzVoWW5OV1lYSWdQQzBnWENKVlFWTk1iM1ZrUlVOTlFWQnZkMEYyWjBKcGJsd2lYRzVjYm1CZ1lDSjkgLS0+XG5cbmBgYHJcblxuYWJzVmFyIDwtIFxcVUFTTG91ZEVDTUFQb3dBdmdCaW5cXFxuXG5gYGBcblxuPCEtLSBybmItc291cmNlLWVuZCAtLT5cbiJ9 -->

absVar <- \UASLoudECMAPowAvgBin\



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dVhHNWhZbk5XWVhJZ1BDMGdYRnhWUVZOTWIzVmtSVU5OUVZCdmQwRjJaMEpwYmx4Y1hHNWNibUJnWUZ4dVlHQmdJbjA9IC0tPlxuXG5gYGByXG5gYGByXG5cbmFic1ZhciA8LSBcXFVBU0xvdWRFQ01BUG93QXZnQmluXFxcblxuYGBgXG5gYGBcblxuPCEtLSBybmItc291cmNlLWVuZCAtLT5cbiJ9 -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5hYnNWYXIgPC0gXFxVQVNMb3VkRUNNQVBvd0F2Z0JpblxcXG5cbmBgYFxuYGBgIn0= -->

```r
```r

absVar <- \UASLoudECMAPowAvgBin\

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


### SQM analysis

#### Replace ambient only stimuli - OMITTED

Here, the 'ambient only' stimuli are replaced, as the SQM analysis can incorporate 0 values (whereas dB metrics cannot be handled in this way).


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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# 
# stimDataNum <- rbind(stimDataNum, stimData[stimData['Stimulus']
#                                               == \A1\,
#                                               colnames(stimDataNum)],
#                       stimData[stimData['Stimulus']
#                                               == \A2\,
#                                               colnames(stimDataNum)])
# stimDataNum <- arrange(stimDataNum, Stimulus)



<!-- rnb-output-end -->

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```r
```r
# 
# stimDataNum <- rbind(stimDataNum, stimData[stimData['Stimulus']
#                                               == \A1\,
#                                               colnames(stimDataNum)],
#                       stimData[stimData['Stimulus']
#                                               == \A2\,
#                                               colnames(stimDataNum)])
# stimDataNum <- arrange(stimDataNum, Stimulus)

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


#### Individual SQMs

##### Sharpness

###### Set variables


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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iVars <- c(absVar, eventVar, ambVar, \UASSharpAurISO3PowAvgMaxLR\, \UASSharpAurISO305ExMaxLR\, \UASSharpAurSHMPowAvgMaxLR\, \UASSharpAurSHM05ExMaxLR\, \UASSharpAurISO1PowAvgMaxLR\, \UASSharpAurISO105ExMaxLR\, \UASSharpvBISO1PowAvgMaxLR\, \UASSharpvBISO105ExMaxLR\, \UASSharpDINPowAvgMaxLR\, \UASSharpDIN05ExMaxLR\)
dVar <- \dHighAnnoyPc\

seeds <- c(7041, 905, 4984651, 6513213, 120651)



<!-- rnb-output-end -->

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```r
```r

iVars <- c(absVar, eventVar, ambVar, \UASSharpAurISO3PowAvgMaxLR\, \UASSharpAurISO305ExMaxLR\, \UASSharpAurSHMPowAvgMaxLR\, \UASSharpAurSHM05ExMaxLR\, \UASSharpAurISO1PowAvgMaxLR\, \UASSharpAurISO105ExMaxLR\, \UASSharpvBISO1PowAvgMaxLR\, \UASSharpvBISO105ExMaxLR\, \UASSharpDINPowAvgMaxLR\, \UASSharpDIN05ExMaxLR\)
dVar <- \dHighAnnoyPc\

seeds <- c(7041, 905, 4984651, 6513213, 120651)

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


###### Hyperparameter tuning


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p



<!-- rnb-output-end -->

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```r
```r

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Selected hyperparameters


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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ntree <- 1001
mtry <- as.integer(length(iVars)/1.5)



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dVhHNXVkSEpsWlNBOExTQXhNREF4WEc1dGRISjVJRHd0SUdGekxtbHVkR1ZuWlhJb2JHVnVaM1JvS0dsV1lYSnpLUzh4TGpVcFhHNWNibUJnWUZ4dVlHQmdJbjA9IC0tPlxuXG5gYGByXG5gYGByXG5cbm50cmVlIDwtIDEwMDFcbm10cnkgPC0gYXMuaW50ZWdlcihsZW5ndGgoaVZhcnMpLzEuNSlcblxuYGBgXG5gYGBcblxuPCEtLSBybmItc291cmNlLWVuZCAtLT5cbiJ9 -->


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```r
```r

ntree <- 1001
mtry <- as.integer(length(iVars)/1.5)

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


###### Run model

Train preliminary model


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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nperm <- 5

resultsOutSharp <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSharp$OOB_RMSE
resultsOutSharp$OOB_MAE
resultsOutSharp$Rsquared



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


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```r
```r

nperm <- 5

resultsOutSharp <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSharp$OOB_RMSE
resultsOutSharp$OOB_MAE
resultsOutSharp$Rsquared

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->

Train multiple seeds model


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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resultsOutSharp <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSharp$OOB_RMSE
resultsOutSharp$OOB_MAE
resultsOutSharp$Rsquared



<!-- rnb-output-end -->

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```r
```r

resultsOutSharp <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSharp$OOB_RMSE
resultsOutSharp$OOB_MAE
resultsOutSharp$Rsquared

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->




<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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# store results
resdHiAnnoyFitAB['Abs sharp', 'RMSE'] <- resultsOutSharp$OOB_RMSE
resdHiAnnoyFitAB['Abs sharp', 'MAE'] <- resultsOutSharp$OOB_MAE
resdHiAnnoyFitAB['Abs sharp', 'Rsquared'] <- resultsOutSharp$Rsquared
resdHiAnnoyPermImpAB$AbsSharp <- resultsOutSharp$conditional_permimp



<!-- rnb-output-end -->

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```r
```r
# store results
resdHiAnnoyFitAB['Abs sharp', 'RMSE'] <- resultsOutSharp$OOB_RMSE
resdHiAnnoyFitAB['Abs sharp', 'MAE'] <- resultsOutSharp$OOB_MAE
resdHiAnnoyFitAB['Abs sharp', 'Rsquared'] <- resultsOutSharp$Rsquared
resdHiAnnoyPermImpAB$AbsSharp <- resultsOutSharp$conditional_permimp

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


###### Plot results


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutSharp.conimp <- arrange(resultsOutSharp$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutSharp.conimp) + geom_col(aes(x=factor(rownames(resultsOutSharp.conimp), levels=rownames(resultsOutSharp.conimp)), y=CondPermImp), fill=mycolours[2], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (% highly annoyed)\) + ggtitle(\Sharpness\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename=\PtsABdHiAnnoySharpConPermimp.svg\, width=8, height=4.9, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdHiAnnoySharpConPermimp.svg\)
  
  ggsave(filename=\PtsABdHiAnnoySharpConPermimp.pdf\, width=8, height=4.9, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdHiAnnoySharpConPermimp.pdf\)
}



<!-- rnb-output-end -->

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```r
```r
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutSharp.conimp <- arrange(resultsOutSharp$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutSharp.conimp) + geom_col(aes(x=factor(rownames(resultsOutSharp.conimp), levels=rownames(resultsOutSharp.conimp)), y=CondPermImp), fill=mycolours[2], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (% highly annoyed)\) + ggtitle(\Sharpness\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename=\PtsABdHiAnnoySharpConPermimp.svg\, width=8, height=4.9, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdHiAnnoySharpConPermimp.svg\)
  
  ggsave(filename=\PtsABdHiAnnoySharpConPermimp.pdf\, width=8, height=4.9, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdHiAnnoySharpConPermimp.pdf\)
}

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Selected metric


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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sharpVar <- \UASSharpAurISO305ExMaxLR\



<!-- rnb-output-end -->

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```r
```r

sharpVar <- \UASSharpAurISO305ExMaxLR\

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->



##### Tonal loudness and tonality

###### Set variables


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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iVars <- c(absVar, eventVar, ambVar, \UASTonalECMAAvgMaxLR\, \UASTonalSHMInt05ExMaxLR\, \UASTonalSHMIntAvgMaxLR\, \UASTonalECMA05ExMaxLR\, \UASTonLdECMAPowAvgBin\, \UASTonLdECMA05ExBin\, \UASTonalAurAvgMaxLR\, \UASTonalAur05ExMaxLR\, \UASTonalAur10ExMaxLR\)
dVar <- \dHighAnnoyPc\

seeds <- c(540, 104798, 456464, 87331, 94564)



<!-- rnb-output-end -->

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```r
```r

iVars <- c(absVar, eventVar, ambVar, \UASTonalECMAAvgMaxLR\, \UASTonalSHMInt05ExMaxLR\, \UASTonalSHMIntAvgMaxLR\, \UASTonalECMA05ExMaxLR\, \UASTonLdECMAPowAvgBin\, \UASTonLdECMA05ExBin\, \UASTonalAurAvgMaxLR\, \UASTonalAur05ExMaxLR\, \UASTonalAur10ExMaxLR\)
dVar <- \dHighAnnoyPc\

seeds <- c(540, 104798, 456464, 87331, 94564)

```
```

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###### Hyperparameter tuning


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p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p



<!-- rnb-output-end -->

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<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5wIDwtIG10cnlUdW5lKGRhdGFJbj1zdGltRGF0YU51bSwgaVZhcnM9aVZhcnMsIGRWYXI9ZFZhciwgc2VlZD1zZWVkc1sxOjJdLFxuICAgICAgICAgICAgIG50cmVlcz1udHJlZXMsIG1pbnNwbGl0PW1pbnNwbGl0LCBtaW5idWNrZXQ9bWluYnVja2V0KVxucFxuXG5gYGBcbmBgYCJ9 -->

```r
```r

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```
```

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Selected hyperparameters


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ntree <- 501
mtry <- as.integer(length(iVars)/1.25)



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```r
```r

ntree <- 501
mtry <- as.integer(length(iVars)/1.25)

```
```

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###### Run model

Train preliminary model


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# Tonality with tonal loudness

nperm <- 5

resultsOutTonal1 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutTonal1$OOB_RMSE
resultsOutTonal1$OOB_MAE
resultsOutTonal1$Rsquared



<!-- rnb-output-end -->

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```r
```r
# Tonality with tonal loudness

nperm <- 5

resultsOutTonal1 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutTonal1$OOB_RMSE
resultsOutTonal1$OOB_MAE
resultsOutTonal1$Rsquared
```
```

<!-- rnb-source-end -->


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<!-- rnb-text-begin -->


Train multiple seeds model


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# Tonality with tonal loudness

resultsOutTonal1 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutTonal1$OOB_RMSE
resultsOutTonal1$OOB_MAE
resultsOutTonal1$Rsquared



<!-- rnb-output-end -->

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```r
```r
# Tonality with tonal loudness

resultsOutTonal1 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutTonal1$OOB_RMSE
resultsOutTonal1$OOB_MAE
resultsOutTonal1$Rsquared

```
```

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# store results
resdHiAnnoyFitAB['Abs tonal inc loud', 'RMSE'] <- resultsOutTonal1$OOB_RMSE
resdHiAnnoyFitAB['Abs tonal inc loud', 'MAE'] <- resultsOutTonal1$OOB_MAE
resdHiAnnoyFitAB['Abs tonal inc loud', 'Rsquared'] <- resultsOutTonal1$Rsquared
resdHiAnnoyPermImpAB$AbsTonal1 <- resultsOutTonal1$conditional_permimp



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```r
```r
# store results
resdHiAnnoyFitAB['Abs tonal inc loud', 'RMSE'] <- resultsOutTonal1$OOB_RMSE
resdHiAnnoyFitAB['Abs tonal inc loud', 'MAE'] <- resultsOutTonal1$OOB_MAE
resdHiAnnoyFitAB['Abs tonal inc loud', 'Rsquared'] <- resultsOutTonal1$Rsquared
resdHiAnnoyPermImpAB$AbsTonal1 <- resultsOutTonal1$conditional_permimp

```
```

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###### Plot results


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par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutTonal1.conimp <- arrange(resultsOutTonal1$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutTonal1.conimp) + geom_col(aes(x=factor(rownames(resultsOutTonal1.conimp), levels=rownames(resultsOutTonal1.conimp)), y=CondPermImp), fill=mycolours[3], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (% highly annoyed)\) + ggtitle(\Tonality inc. tonal loudness\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 110))
pBar

if (saveplots){
  ggsave(filename=\PtsABdHiAnnoyTonalLdConPermimp.svg\, width=8, height=3.8, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdHiAnnoyTonalLdConPermimp.svg\)
  
  ggsave(filename=\PtsABdHiAnnoyTonalLdConPermimp.pdf\, width=8, height=3.8, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdHiAnnoyTonalLdConPermimp.pdf\)
}



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```r
```r

par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutTonal1.conimp <- arrange(resultsOutTonal1$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutTonal1.conimp) + geom_col(aes(x=factor(rownames(resultsOutTonal1.conimp), levels=rownames(resultsOutTonal1.conimp)), y=CondPermImp), fill=mycolours[3], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (% highly annoyed)\) + ggtitle(\Tonality inc. tonal loudness\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 110))
pBar

if (saveplots){
  ggsave(filename=\PtsABdHiAnnoyTonalLdConPermimp.svg\, width=8, height=3.8, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdHiAnnoyTonalLdConPermimp.svg\)
  
  ggsave(filename=\PtsABdHiAnnoyTonalLdConPermimp.pdf\, width=8, height=3.8, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdHiAnnoyTonalLdConPermimp.pdf\)
}

```
```

<!-- rnb-source-end -->


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<!-- rnb-text-begin -->


Selected metric


<!-- rnb-text-end -->


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tonLdVar <- \UASTonLdECMAPowAvgBin\



<!-- rnb-output-end -->

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```r
```r

tonLdVar <- \UASTonLdECMAPowAvgBin\

```
```

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##### Tonality without tonal loudness

###### Set variables


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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iVars <- c(absVar, eventVar, ambVar, \UASTonalECMAAvgMaxLR\, \UASTonalSHMInt05ExMaxLR\, \UASTonalSHMIntAvgMaxLR\, \UASTonalECMA05ExMaxLR\, \UASTonalAurAvgMaxLR\, \UASTonalAur05ExMaxLR\, \UASTonalAur10ExMaxLR\)
dVar <- \dHighAnnoyPc\

seeds <- c(156089, 5860, 10528, 89541, 4685146)



<!-- rnb-output-end -->

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```r
```r

iVars <- c(absVar, eventVar, ambVar, \UASTonalECMAAvgMaxLR\, \UASTonalSHMInt05ExMaxLR\, \UASTonalSHMIntAvgMaxLR\, \UASTonalECMA05ExMaxLR\, \UASTonalAurAvgMaxLR\, \UASTonalAur05ExMaxLR\, \UASTonalAur10ExMaxLR\)
dVar <- \dHighAnnoyPc\

seeds <- c(156089, 5860, 10528, 89541, 4685146)

```
```

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###### Hyperparameter tuning


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p



<!-- rnb-output-end -->

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<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5wIDwtIG10cnlUdW5lKGRhdGFJbj1zdGltRGF0YU51bSwgaVZhcnM9aVZhcnMsIGRWYXI9ZFZhciwgc2VlZD1zZWVkc1sxOjJdLFxuICAgICAgICAgICAgIG50cmVlcz1udHJlZXMsIG1pbnNwbGl0PW1pbnNwbGl0LCBtaW5idWNrZXQ9bWluYnVja2V0KVxucFxuXG5gYGBcbmBgYCJ9 -->

```r
```r

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Selected hyperparameters


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVYRzV1ZEhKbFpTQThMU0ExTlRBeFhHNXRkSEo1SUR3dElHRnpMbWx1ZEdWblpYSW9iR1Z1WjNSb0tHbFdZWEp6S1M4eExqSTFLVnh1WEc1Z1lHQWlmUT09IC0tPlxuXG5gYGByXG5cbm50cmVlIDwtIDU1MDFcbm10cnkgPC0gYXMuaW50ZWdlcihsZW5ndGgoaVZhcnMpLzEuMjUpXG5cbmBgYFxuXG48IS0tIHJuYi1zb3VyY2UtZW5kIC0tPlxuIn0= -->

ntree <- 5501
mtry <- as.integer(length(iVars)/1.25)



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dVhHNXVkSEpsWlNBOExTQTFOVEF4WEc1dGRISjVJRHd0SUdGekxtbHVkR1ZuWlhJb2JHVnVaM1JvS0dsV1lYSnpLUzh4TGpJMUtWeHVYRzVnWUdCY2JtQmdZQ0o5IC0tPlxuXG5gYGByXG5gYGByXG5cbm50cmVlIDwtIDU1MDFcbm10cnkgPC0gYXMuaW50ZWdlcihsZW5ndGgoaVZhcnMpLzEuMjUpXG5cbmBgYFxuYGBgXG5cbjwhLS0gcm5iLXNvdXJjZS1lbmQgLS0+XG4ifQ== -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5udHJlZSA8LSA1NTAxXG5tdHJ5IDwtIGFzLmludGVnZXIobGVuZ3RoKGlWYXJzKS8xLjI1KVxuXG5gYGBcbmBgYCJ9 -->

```r
```r

ntree <- 5501
mtry <- as.integer(length(iVars)/1.25)

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->



###### Run model

Train preliminary model


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->
# Tonality

nperm <- 5

resultsOutTonal2 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
                           ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm,
                           minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutTonal2$OOB_RMSE
resultsOutTonal2$OOB_MAE
resultsOutTonal2$Rsquared



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


<!-- rnb-source-begin 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 -->

```r
```r
# Tonality

nperm <- 5

resultsOutTonal2 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
                           ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm,
                           minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutTonal2$OOB_RMSE
resultsOutTonal2$OOB_MAE
resultsOutTonal2$Rsquared

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Train multiple seeds model


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->
# Tonality

resultsOutTonal2 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutTonal2$OOB_RMSE
resultsOutTonal2$OOB_MAE
resultsOutTonal2$Rsquared



<!-- rnb-output-end -->

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```r
```r
# Tonality

resultsOutTonal2 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutTonal2$OOB_RMSE
resultsOutTonal2$OOB_MAE
resultsOutTonal2$Rsquared

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->




<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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# store results
resdHiAnnoyFitAB['Abs tonal no loud', 'RMSE'] <- resultsOutTonal2$OOB_RMSE
resdHiAnnoyFitAB['Abs tonal no loud', 'MAE'] <- resultsOutTonal2$OOB_MAE
resdHiAnnoyFitAB['Abs tonal no loud', 'Rsquared'] <- resultsOutTonal2$Rsquared
resdHiAnnoyPermImpAB$AbsTonal2 <- resultsOutTonal2$conditional_permimp



<!-- rnb-output-end -->

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```r
```r

# store results
resdHiAnnoyFitAB['Abs tonal no loud', 'RMSE'] <- resultsOutTonal2$OOB_RMSE
resdHiAnnoyFitAB['Abs tonal no loud', 'MAE'] <- resultsOutTonal2$OOB_MAE
resdHiAnnoyFitAB['Abs tonal no loud', 'Rsquared'] <- resultsOutTonal2$Rsquared
resdHiAnnoyPermImpAB$AbsTonal2 <- resultsOutTonal2$conditional_permimp

```
```

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###### Plot results


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par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutTonal2.conimp <- arrange(resultsOutTonal2$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutTonal2.conimp) + geom_col(aes(x=factor(rownames(resultsOutTonal2.conimp), levels=rownames(resultsOutTonal2.conimp)), y=CondPermImp), fill=mycolours[3], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (% highly annoyed)\) + ggtitle(\Tonality w/o tonal loudness\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 110))
pBar

if (saveplots){
  ggsave(filename=\PtsABdHiAnnoyTonalConPermimp.svg\, width=8, height=3.2, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdHiAnnoyTonalConPermimp.svg\)
  
  ggsave(filename=\PtsABdHiAnnoyTonalConPermimp.pdf\, width=8, height=3.2, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdHiAnnoyTonalConPermimp.pdf\)
}



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```r
```r
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutTonal2.conimp <- arrange(resultsOutTonal2$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutTonal2.conimp) + geom_col(aes(x=factor(rownames(resultsOutTonal2.conimp), levels=rownames(resultsOutTonal2.conimp)), y=CondPermImp), fill=mycolours[3], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (% highly annoyed)\) + ggtitle(\Tonality w/o tonal loudness\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 110))
pBar

if (saveplots){
  ggsave(filename=\PtsABdHiAnnoyTonalConPermimp.svg\, width=8, height=3.2, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdHiAnnoyTonalConPermimp.svg\)
  
  ggsave(filename=\PtsABdHiAnnoyTonalConPermimp.pdf\, width=8, height=3.2, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdHiAnnoyTonalConPermimp.pdf\)
}

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Selected metric


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVYRzUwYjI1aGJGWmhjaUE4TFNCY0lsVkJVMVJ2Ym1Gc1UwaE5TVzUwUVhablRXRjRURkpjSWx4dVhHNWdZR0FpZlE9PSAtLT5cblxuYGBgclxuXG50b25hbFZhciA8LSBcXFVBU1RvbmFsU0hNSW50QXZnTWF4TFJcXFxuXG5gYGBcblxuPCEtLSBybmItc291cmNlLWVuZCAtLT5cbiJ9 -->

tonalVar <- \UASTonalSHMIntAvgMaxLR\



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dVhHNTBiMjVoYkZaaGNpQThMU0JjWEZWQlUxUnZibUZzVTBoTlNXNTBRWFpuVFdGNFRGSmNYRnh1WEc1Z1lHQmNibUJnWUNKOSAtLT5cblxuYGBgclxuYGBgclxuXG50b25hbFZhciA8LSBcXFVBU1RvbmFsU0hNSW50QXZnTWF4TFJcXFxuXG5gYGBcbmBgYFxuXG48IS0tIHJuYi1zb3VyY2UtZW5kIC0tPlxuIn0= -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG50b25hbFZhciA8LSBcXFVBU1RvbmFsU0hNSW50QXZnTWF4TFJcXFxuXG5gYGBcbmBgYCJ9 -->

```r
```r

tonalVar <- \UASTonalSHMIntAvgMaxLR\

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


##### Fluctuation strength

###### Set variables


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->

# Fluctuation strength
iVars <- c(absVar, eventVar, ambVar, \UASFluctSHM10ExBin\, \UASFluctSHM05ExBin\, \UASFluctFZ10ExMaxLR\, \UASFluctFZ05ExMaxLR\, \UASFluctOV10ExMaxLR\, \UASFluctOV05ExMaxLR\)
dVar <- \dHighAnnoyPc\

seeds <- c(25107, 546098, 195, 5937, 102658)



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG4jIEZsdWN0dWF0aW9uIHN0cmVuZ3RoXG5pVmFycyA8LSBjKGFic1ZhciwgZXZlbnRWYXIsIGFtYlZhciwgXFxVQVNGbHVjdFNITTEwRXhCaW5cXCwgXFxVQVNGbHVjdFNITTA1RXhCaW5cXCwgXFxVQVNGbHVjdEZaMTBFeE1heExSXFwsIFxcVUFTRmx1Y3RGWjA1RXhNYXhMUlxcLCBcXFVBU0ZsdWN0T1YxMEV4TWF4TFJcXCwgXFxVQVNGbHVjdE9WMDVFeE1heExSXFwpXG5kVmFyIDwtIFxcZEhpZ2hBbm5veVBjXFxcblxuc2VlZHMgPC0gYygyNTEwNywgNTQ2MDk4LCAxOTUsIDU5MzcsIDEwMjY1OClcblxuYGBgXG5gYGAifQ== -->

```r
```r

# Fluctuation strength
iVars <- c(absVar, eventVar, ambVar, \UASFluctSHM10ExBin\, \UASFluctSHM05ExBin\, \UASFluctFZ10ExMaxLR\, \UASFluctFZ05ExMaxLR\, \UASFluctOV10ExMaxLR\, \UASFluctOV05ExMaxLR\)
dVar <- \dHighAnnoyPc\

seeds <- c(25107, 546098, 195, 5937, 102658)

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


###### Hyperparameter tuning


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5wIDwtIG10cnlUdW5lKGRhdGFJbj1zdGltRGF0YU51bSwgaVZhcnM9aVZhcnMsIGRWYXI9ZFZhciwgc2VlZD1zZWVkc1sxOjJdLFxuICAgICAgICAgICAgIG50cmVlcz1udHJlZXMsIG1pbnNwbGl0PW1pbnNwbGl0LCBtaW5idWNrZXQ9bWluYnVja2V0KVxucFxuXG5gYGBcbmBgYCJ9 -->

```r
```r

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Selected hyperparameters


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVYRzV1ZEhKbFpTQThMU0ExTURGY2JtMTBjbmtnUEMwZ1lYTXVhVzUwWldkbGNpaHNaVzVuZEdnb2FWWmhjbk1wTHpFdU1qVXBYRzVjYm1CZ1lDSjkgLS0+XG5cbmBgYHJcblxubnRyZWUgPC0gNTAxXG5tdHJ5IDwtIGFzLmludGVnZXIobGVuZ3RoKGlWYXJzKS8xLjI1KVxuXG5gYGBcblxuPCEtLSBybmItc291cmNlLWVuZCAtLT5cbiJ9 -->

ntree <- 501
mtry <- as.integer(length(iVars)/1.25)



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dVhHNXVkSEpsWlNBOExTQTFNREZjYm0xMGNua2dQQzBnWVhNdWFXNTBaV2RsY2loc1pXNW5kR2dvYVZaaGNuTXBMekV1TWpVcFhHNWNibUJnWUZ4dVlHQmdJbjA9IC0tPlxuXG5gYGByXG5gYGByXG5cbm50cmVlIDwtIDUwMVxubXRyeSA8LSBhcy5pbnRlZ2VyKGxlbmd0aChpVmFycykvMS4yNSlcblxuYGBgXG5gYGBcblxuPCEtLSBybmItc291cmNlLWVuZCAtLT5cbiJ9 -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5udHJlZSA8LSA1MDFcbm10cnkgPC0gYXMuaW50ZWdlcihsZW5ndGgoaVZhcnMpLzEuMjUpXG5cbmBgYFxuYGBgIn0= -->

```r
```r

ntree <- 501
mtry <- as.integer(length(iVars)/1.25)

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


###### Run model

Train preliminary model


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVYRzV1Y0dWeWJTQThMU0ExWEc1Y2JuSmxjM1ZzZEhOUGRYUkdiSFZqZENBOExTQmpjbVpTWldjb1pHRjBZVWx1UFhOMGFXMUVZWFJoVG5WdExDQnBWbUZ5Y3oxcFZtRnljeXdnWkZaaGNqMWtWbUZ5TENCelpXVmtjejF6WldWa2Mxc3hPakpkTEZ4dUlDQWdJQ0FnSUNBZ0lDQWdJQ0FnSUNBZ0lDQWdJQ0FnSUNCdWRISmxaVDF1ZEhKbFpTd2diWFJ5ZVQxdGRISjVMQ0J3WlhKdFNXMXdRMjl1WkZSb2NtVnpQWEJsY20xSmJYQkRiMjVrVkdoeVpYTXNYRzRnSUNBZ0lDQWdJQ0FnSUNBZ0lDQWdJQ0FnSUNBZ0lDQWdJRzV3WlhKdFBXNXdaWEp0TENCdGFXNXpjR3hwZEQxdGFXNXpjR3hwZEN3Z2JXbHVZblZqYTJWMFBXMXBibUoxWTJ0bGRDbGNibHh1SXlCd2NtbHVkQ0J0YjJSbGJDQndjbVZrYVdOMGFXOXVJSEpsYzNWc2RITmNibkpsYzNWc2RITlBkWFJHYkhWamRDUlBUMEpmVWsxVFJWeHVjbVZ6ZFd4MGMwOTFkRVpzZFdOMEpFOVBRbDlOUVVWY2JuSmxjM1ZzZEhOUGRYUkdiSFZqZENSU2MzRjFZWEpsWkZ4dVhHNWdZR0FpZlE9PSAtLT5cblxuYGBgclxuXG5ucGVybSA8LSA1XG5cbnJlc3VsdHNPdXRGbHVjdCA8LSBjcmZSZWcoZGF0YUluPXN0aW1EYXRhTnVtLCBpVmFycz1pVmFycywgZFZhcj1kVmFyLCBzZWVkcz1zZWVkc1sxOjJdLFxuICAgICAgICAgICAgICAgICAgICAgICAgICBudHJlZT1udHJlZSwgbXRyeT1tdHJ5LCBwZXJtSW1wQ29uZFRocmVzPXBlcm1JbXBDb25kVGhyZXMsXG4gICAgICAgICAgICAgICAgICAgICAgICAgIG5wZXJtPW5wZXJtLCBtaW5zcGxpdD1taW5zcGxpdCwgbWluYnVja2V0PW1pbmJ1Y2tldClcblxuIyBwcmludCBtb2RlbCBwcmVkaWN0aW9uIHJlc3VsdHNcbnJlc3VsdHNPdXRGbHVjdCRPT0JfUk1TRVxucmVzdWx0c091dEZsdWN0JE9PQl9NQUVcbnJlc3VsdHNPdXRGbHVjdCRSc3F1YXJlZFxuXG5gYGBcblxuPCEtLSBybmItc291cmNlLWVuZCAtLT5cbiJ9 -->

nperm <- 5

resultsOutFluct <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
                          ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres,
                          nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutFluct$OOB_RMSE
resultsOutFluct$OOB_MAE
resultsOutFluct$Rsquared



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


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```r
```r

nperm <- 5

resultsOutFluct <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
                          ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres,
                          nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutFluct$OOB_RMSE
resultsOutFluct$OOB_MAE
resultsOutFluct$Rsquared

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->

Train multiple seeds model


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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resultsOutFluct <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutFluct$OOB_RMSE
resultsOutFluct$OOB_MAE
resultsOutFluct$Rsquared



<!-- rnb-output-end -->

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```r
```r

resultsOutFluct <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutFluct$OOB_RMSE
resultsOutFluct$OOB_MAE
resultsOutFluct$Rsquared

```
```

<!-- rnb-source-end -->


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<!-- rnb-text-begin -->




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# store results
resdHiAnnoyFitAB['Abs fluct', 'RMSE'] <- resultsOutFluct$OOB_RMSE
resdHiAnnoyFitAB['Abs fluct', 'MAE'] <- resultsOutFluct$OOB_MAE
resdHiAnnoyFitAB['Abs fluct', 'Rsquared'] <- resultsOutFluct$Rsquared
resdHiAnnoyPermImpAB$AbsFluct <- resultsOutFluct$conditional_permimp



<!-- rnb-output-end -->

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```r
```r

# store results
resdHiAnnoyFitAB['Abs fluct', 'RMSE'] <- resultsOutFluct$OOB_RMSE
resdHiAnnoyFitAB['Abs fluct', 'MAE'] <- resultsOutFluct$OOB_MAE
resdHiAnnoyFitAB['Abs fluct', 'Rsquared'] <- resultsOutFluct$Rsquared
resdHiAnnoyPermImpAB$AbsFluct <- resultsOutFluct$conditional_permimp

```
```

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###### Plot results


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<!-- rnb-chunk-begin -->


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par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutFluct.conimp <- arrange(resultsOutFluct$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutFluct.conimp) + geom_col(aes(x=factor(rownames(resultsOutFluct.conimp), levels=rownames(resultsOutFluct.conimp)), y=CondPermImp), fill=mycolours[4], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (% highly annoyed)\) + ggtitle(\Fluctuation strength\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename=\PtsABdHiAnnoyFluctConPermimp.svg\, width=8, height=2.9, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdHiAnnoyFluctConPermimp.svg\)
  
  ggsave(filename=\PtsABdHiAnnoyFluctConPermimp.pdf\, width=8, height=2.9, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdHiAnnoyFluctConPermimp.pdf\)
}



<!-- rnb-output-end -->

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```r
```r
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutFluct.conimp <- arrange(resultsOutFluct$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutFluct.conimp) + geom_col(aes(x=factor(rownames(resultsOutFluct.conimp), levels=rownames(resultsOutFluct.conimp)), y=CondPermImp), fill=mycolours[4], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (% highly annoyed)\) + ggtitle(\Fluctuation strength\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename=\PtsABdHiAnnoyFluctConPermimp.svg\, width=8, height=2.9, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdHiAnnoyFluctConPermimp.svg\)
  
  ggsave(filename=\PtsABdHiAnnoyFluctConPermimp.pdf\, width=8, height=2.9, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdHiAnnoyFluctConPermimp.pdf\)
}

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Selected metric


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVYRzVtYkhWamRGWmhjaUE4TFNCY0lsVkJVMFpzZFdOMFQxWXhNRVY0VFdGNFRGSmNJbHh1WEc1Z1lHQWlmUT09IC0tPlxuXG5gYGByXG5cbmZsdWN0VmFyIDwtIFxcVUFTRmx1Y3RPVjEwRXhNYXhMUlxcXG5cbmBgYFxuXG48IS0tIHJuYi1zb3VyY2UtZW5kIC0tPlxuIn0= -->

fluctVar <- \UASFluctOV10ExMaxLR\



<!-- rnb-output-end -->

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```r
```r

fluctVar <- \UASFluctOV10ExMaxLR\

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


##### Roughness

###### Set variables


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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# Roughness
iVars <- c(absVar, eventVar, ambVar, \UASRoughECMA10ExBin\, \UASRoughECMA05ExBin\, \UASRoughFZ10ExMaxLR\, \UASRoughFZ05ExMaxLR\, \UASRoughDW10ExMaxLR\, \UASRoughDW05ExMaxLR\)
dVar <- \dHighAnnoyPc\

seeds <- c(4701, 52187, 16589, 65217, 16893)



<!-- rnb-output-end -->

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```r
```r

# Roughness
iVars <- c(absVar, eventVar, ambVar, \UASRoughECMA10ExBin\, \UASRoughECMA05ExBin\, \UASRoughFZ10ExMaxLR\, \UASRoughFZ05ExMaxLR\, \UASRoughDW10ExMaxLR\, \UASRoughDW05ExMaxLR\)
dVar <- \dHighAnnoyPc\

seeds <- c(4701, 52187, 16589, 65217, 16893)

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


###### Hyperparameter tuning


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p



<!-- rnb-output-end -->

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```r
```r

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Selected hyperparameters


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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ntree <- 2501
mtry <- as.integer(length(iVars)/1.25)



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dVhHNXVkSEpsWlNBOExTQXlOVEF4WEc1dGRISjVJRHd0SUdGekxtbHVkR1ZuWlhJb2JHVnVaM1JvS0dsV1lYSnpLUzh4TGpJMUtWeHVYRzVnWUdCY2JtQmdZQ0o5IC0tPlxuXG5gYGByXG5gYGByXG5cbm50cmVlIDwtIDI1MDFcbm10cnkgPC0gYXMuaW50ZWdlcihsZW5ndGgoaVZhcnMpLzEuMjUpXG5cbmBgYFxuYGBgXG5cbjwhLS0gcm5iLXNvdXJjZS1lbmQgLS0+XG4ifQ== -->


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```r
```r

ntree <- 2501
mtry <- as.integer(length(iVars)/1.25)

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->



###### Run model

Train preliminary model


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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nperm <- 5

resultsOutRough <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutRough$OOB_RMSE
resultsOutRough$OOB_MAE
resultsOutRough$Rsquared



<!-- rnb-output-end -->

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```r
```r

nperm <- 5

resultsOutRough <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutRough$OOB_RMSE
resultsOutRough$OOB_MAE
resultsOutRough$Rsquared

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Train multiple seeds model


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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resultsOutRough <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutRough$OOB_RMSE
resultsOutRough$OOB_MAE
resultsOutRough$Rsquared



<!-- rnb-output-end -->

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```r
```r

resultsOutRough <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutRough$OOB_RMSE
resultsOutRough$OOB_MAE
resultsOutRough$Rsquared

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->



<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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# store results
resdHiAnnoyFitAB['Abs rough', 'RMSE'] <- resultsOutRough$OOB_RMSE
resdHiAnnoyFitAB['Abs rough', 'MAE'] <- resultsOutRough$OOB_MAE
resdHiAnnoyFitAB['Abs rough', 'Rsquared'] <- resultsOutRough$Rsquared
resdHiAnnoyPermImpAB$AbsRough <- resultsOutRough$conditional_permimp



<!-- rnb-output-end -->

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```r
```r
# store results
resdHiAnnoyFitAB['Abs rough', 'RMSE'] <- resultsOutRough$OOB_RMSE
resdHiAnnoyFitAB['Abs rough', 'MAE'] <- resultsOutRough$OOB_MAE
resdHiAnnoyFitAB['Abs rough', 'Rsquared'] <- resultsOutRough$Rsquared
resdHiAnnoyPermImpAB$AbsRough <- resultsOutRough$conditional_permimp

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


###### Plot results


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutRough.conimp <- arrange(resultsOutRough$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutRough.conimp) + geom_col(aes(x=factor(rownames(resultsOutRough.conimp), levels=rownames(resultsOutRough.conimp)), y=CondPermImp), fill=mycolours[5], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (% highly annoyed)\) + ggtitle(\Roughness\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename=\PtsABdHiAnnoyRoughConPermimp.svg\, width=8, height=2.9, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdHiAnnoyRoughConPermimp.svg\)
  
  ggsave(filename=\PtsABdHiAnnoyRoughConPermimp.pdf\, width=8, height=2.9, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdHiAnnoyRoughConPermimp.pdf\)
}



<!-- rnb-output-end -->

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```r
```r
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutRough.conimp <- arrange(resultsOutRough$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutRough.conimp) + geom_col(aes(x=factor(rownames(resultsOutRough.conimp), levels=rownames(resultsOutRough.conimp)), y=CondPermImp), fill=mycolours[5], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (% highly annoyed)\) + ggtitle(\Roughness\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename=\PtsABdHiAnnoyRoughConPermimp.svg\, width=8, height=2.9, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdHiAnnoyRoughConPermimp.svg\)
  
  ggsave(filename=\PtsABdHiAnnoyRoughConPermimp.pdf\, width=8, height=2.9, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdHiAnnoyRoughConPermimp.pdf\)
}

```
```

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<!-- rnb-text-begin -->


Selected metric


<!-- rnb-text-end -->


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roughVar <- \UASRoughFZ05ExMaxLR\



<!-- rnb-output-end -->

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```r
```r

roughVar <- \UASRoughFZ05ExMaxLR\

```
```

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<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->



##### Impulsiveness

###### Set variables


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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# Impulsiveness
iVars <- c(absVar, eventVar, ambVar, \UASImpulsSHMAvgMaxLR\, \UASImpulsSHM05ExMaxLR\, \UASImpulsSHMPowAvgMaxLR\)
dVar <- \dHighAnnoyPc\

seeds <- c(8495, 59867, 5416, 9843, 86)



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuIyBJbXB1bHNpdmVuZXNzXG5pVmFycyA8LSBjKGFic1ZhciwgZXZlbnRWYXIsIGFtYlZhciwgXFxVQVNJbXB1bHNTSE1BdmdNYXhMUlxcLCBcXFVBU0ltcHVsc1NITTA1RXhNYXhMUlxcLCBcXFVBU0ltcHVsc1NITVBvd0F2Z01heExSXFwpXG5kVmFyIDwtIFxcZEhpZ2hBbm5veVBjXFxcblxuc2VlZHMgPC0gYyg4NDk1LCA1OTg2NywgNTQxNiwgOTg0MywgODYpXG5cbmBgYFxuYGBgIn0= -->

```r
```r
# Impulsiveness
iVars <- c(absVar, eventVar, ambVar, \UASImpulsSHMAvgMaxLR\, \UASImpulsSHM05ExMaxLR\, \UASImpulsSHMPowAvgMaxLR\)
dVar <- \dHighAnnoyPc\

seeds <- c(8495, 59867, 5416, 9843, 86)

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


###### Hyperparameter tuning


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5wIDwtIG10cnlUdW5lKGRhdGFJbj1zdGltRGF0YU51bSwgaVZhcnM9aVZhcnMsIGRWYXI9ZFZhciwgc2VlZD1zZWVkc1sxOjJdLFxuICAgICAgICAgICAgIG50cmVlcz1udHJlZXMsIG1pbnNwbGl0PW1pbnNwbGl0LCBtaW5idWNrZXQ9bWluYnVja2V0KVxucFxuXG5gYGBcbmBgYCJ9 -->

```r
```r

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Selected hyperparameters


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVYRzV1ZEhKbFpTQThMU0ExTURGY2JtMTBjbmtnUEMwZ1lYTXVhVzUwWldkbGNpaHNaVzVuZEdnb2FWWmhjbk1wTHpFdU56VXBYRzVjYm1CZ1lDSjkgLS0+XG5cbmBgYHJcblxubnRyZWUgPC0gNTAxXG5tdHJ5IDwtIGFzLmludGVnZXIobGVuZ3RoKGlWYXJzKS8xLjc1KVxuXG5gYGBcblxuPCEtLSBybmItc291cmNlLWVuZCAtLT5cbiJ9 -->

ntree <- 501
mtry <- as.integer(length(iVars)/1.75)



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dVhHNXVkSEpsWlNBOExTQTFNREZjYm0xMGNua2dQQzBnWVhNdWFXNTBaV2RsY2loc1pXNW5kR2dvYVZaaGNuTXBMekV1TnpVcFhHNWNibUJnWUZ4dVlHQmdJbjA9IC0tPlxuXG5gYGByXG5gYGByXG5cbm50cmVlIDwtIDUwMVxubXRyeSA8LSBhcy5pbnRlZ2VyKGxlbmd0aChpVmFycykvMS43NSlcblxuYGBgXG5gYGBcblxuPCEtLSBybmItc291cmNlLWVuZCAtLT5cbiJ9 -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5udHJlZSA8LSA1MDFcbm10cnkgPC0gYXMuaW50ZWdlcihsZW5ndGgoaVZhcnMpLzEuNzUpXG5cbmBgYFxuYGBgIn0= -->

```r
```r

ntree <- 501
mtry <- as.integer(length(iVars)/1.75)

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->



###### Run model

Train preliminary model


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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nperm <- 5

resultsOutImpuls <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutImpuls$OOB_RMSE
resultsOutImpuls$OOB_MAE
resultsOutImpuls$Rsquared



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


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```r
```r

nperm <- 5

resultsOutImpuls <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutImpuls$OOB_RMSE
resultsOutImpuls$OOB_MAE
resultsOutImpuls$Rsquared

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Train multiple seeds model


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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resultsOutImpuls <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutImpuls$OOB_RMSE
resultsOutImpuls$OOB_MAE
resultsOutImpuls$Rsquared



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dVhHNXlaWE4xYkhSelQzVjBTVzF3ZFd4eklEd3RJRzExYkhScFgyTnlabEpsWnloa1lYUmhTVzQ5YzNScGJVUmhkR0ZPZFcwc0lHbFdZWEp6UFdsV1lYSnpMQ0JrVm1GeVBXUldZWElzSUhObFpXUnpQWE5sWldSekxDQnVkSEpsWlQxdWRISmxaU3dnYlhSeWVUMXRkSEo1TENCd1pYSnRTVzF3UTI5dVpGUm9jbVZ6UFhCbGNtMUpiWEJEYjI1a1ZHaHlaWE1zSUc1d1pYSnRQVzV3WlhKdExDQnRhVzV6Y0d4cGREMXRhVzV6Y0d4cGRDd2diV2x1WW5WamEyVjBQVzFwYm1KMVkydGxkQ2xjYmx4dUl5QndjbWx1ZENCdGIyUmxiQ0J3Y21Wa2FXTjBhVzl1SUhKbGMzVnNkSE5jYm5KbGMzVnNkSE5QZFhSSmJYQjFiSE1rVDA5Q1gxSk5VMFZjYm5KbGMzVnNkSE5QZFhSSmJYQjFiSE1rVDA5Q1gwMUJSVnh1Y21WemRXeDBjMDkxZEVsdGNIVnNjeVJTYzNGMVlYSmxaRnh1WEc1Z1lHQmNibUJnWUNKOSAtLT5cblxuYGBgclxuYGBgclxuXG5yZXN1bHRzT3V0SW1wdWxzIDwtIG11bHRpX2NyZlJlZyhkYXRhSW49c3RpbURhdGFOdW0sIGlWYXJzPWlWYXJzLCBkVmFyPWRWYXIsIHNlZWRzPXNlZWRzLCBudHJlZT1udHJlZSwgbXRyeT1tdHJ5LCBwZXJtSW1wQ29uZFRocmVzPXBlcm1JbXBDb25kVGhyZXMsIG5wZXJtPW5wZXJtLCBtaW5zcGxpdD1taW5zcGxpdCwgbWluYnVja2V0PW1pbmJ1Y2tldClcblxuIyBwcmludCBtb2RlbCBwcmVkaWN0aW9uIHJlc3VsdHNcbnJlc3VsdHNPdXRJbXB1bHMkT09CX1JNU0VcbnJlc3VsdHNPdXRJbXB1bHMkT09CX01BRVxucmVzdWx0c091dEltcHVscyRSc3F1YXJlZFxuXG5gYGBcbmBgYFxuXG48IS0tIHJuYi1zb3VyY2UtZW5kIC0tPlxuIn0= -->


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```r
```r

resultsOutImpuls <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutImpuls$OOB_RMSE
resultsOutImpuls$OOB_MAE
resultsOutImpuls$Rsquared

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->



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<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->

# store results
resdHiAnnoyFitAB['Abs impuls', 'RMSE'] <- resultsOutImpuls$OOB_RMSE
resdHiAnnoyFitAB['Abs impuls', 'MAE'] <- resultsOutImpuls$OOB_MAE
resdHiAnnoyFitAB['Abs impuls', 'Rsquared'] <- resultsOutImpuls$Rsquared
resdHiAnnoyPermImpAB$AbsImpuls <- resultsOutImpuls$conditional_permimp



<!-- rnb-output-end -->

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```r
```r

# store results
resdHiAnnoyFitAB['Abs impuls', 'RMSE'] <- resultsOutImpuls$OOB_RMSE
resdHiAnnoyFitAB['Abs impuls', 'MAE'] <- resultsOutImpuls$OOB_MAE
resdHiAnnoyFitAB['Abs impuls', 'Rsquared'] <- resultsOutImpuls$Rsquared
resdHiAnnoyPermImpAB$AbsImpuls <- resultsOutImpuls$conditional_permimp

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


###### Plot results


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<!-- rnb-chunk-begin -->


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par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutImpuls.conimp <- arrange(resultsOutImpuls$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutImpuls.conimp) + geom_col(aes(x=factor(rownames(resultsOutImpuls.conimp), levels=rownames(resultsOutImpuls.conimp)), y=CondPermImp), fill=mycolours[6], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (% highly annoyed)\) + ggtitle(\Impulsiveness\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename=\PtsABdHiAnnoyImpulsConPermimp.svg\, width=8, height=2, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdHiAnnoyImpulsConPermimp.svg\)
  
  ggsave(filename=\PtsABdHiAnnoyImpulsConPermimp.pdf\, width=8, height=2, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdHiAnnoyImpulsConPermimp.pdf\)
}



<!-- rnb-output-end -->

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```r
```r
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutImpuls.conimp <- arrange(resultsOutImpuls$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutImpuls.conimp) + geom_col(aes(x=factor(rownames(resultsOutImpuls.conimp), levels=rownames(resultsOutImpuls.conimp)), y=CondPermImp), fill=mycolours[6], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (% highly annoyed)\) + ggtitle(\Impulsiveness\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename=\PtsABdHiAnnoyImpulsConPermimp.svg\, width=8, height=2, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdHiAnnoyImpulsConPermimp.svg\)
  
  ggsave(filename=\PtsABdHiAnnoyImpulsConPermimp.pdf\, width=8, height=2, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdHiAnnoyImpulsConPermimp.pdf\)
}

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

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<!-- rnb-text-begin -->


Selected metric


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impulsVar <- \UASImpulsSHMPowAvgMaxLR\



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```r
```r

impulsVar <- \UASImpulsSHMPowAvgMaxLR\

```
```

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<!-- rnb-output-end -->

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<!-- rnb-text-begin -->


#### SQM and loudness comparison

Now the highest importance SQMs are ranked against each other, controlling for UAS loudness and ambient LAeq.

##### Include tonal loudness

###### Set variables


<!-- rnb-text-end -->


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iVars <- c(absVar, eventVar, ambVar, sharpVar, tonLdVar, fluctVar, roughVar, impulsVar)
dVar <- \dHighAnnoyPc\

seeds <- c(70498, 4, 14986, 453, 864)



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```r
```r

iVars <- c(absVar, eventVar, ambVar, sharpVar, tonLdVar, fluctVar, roughVar, impulsVar)
dVar <- \dHighAnnoyPc\

seeds <- c(70498, 4, 14986, 453, 864)

```
```

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<!-- rnb-output-end -->

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<!-- rnb-text-begin -->


###### Hyperparameter tuning


<!-- rnb-text-end -->


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p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p



<!-- rnb-output-end -->

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```r
```r

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

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<!-- rnb-text-begin -->


Selected hyperparameters


<!-- rnb-text-end -->


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ntree <- 1501
mtry <- as.integer(length(iVars)/1.25)



<!-- rnb-output-end -->

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```r
```r

ntree <- 1501
mtry <- as.integer(length(iVars)/1.25)

```
```

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<!-- rnb-output-end -->

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###### Run model

Train preliminary model


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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nperm <- 5

resultsOutSQMs1 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSQMs1$OOB_RMSE
resultsOutSQMs1$OOB_MAE
resultsOutSQMs1$Rsquared



<!-- rnb-output-end -->

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```r
```r

nperm <- 5

resultsOutSQMs1 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSQMs1$OOB_RMSE
resultsOutSQMs1$OOB_MAE
resultsOutSQMs1$Rsquared

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Train multiple seeds model


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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resultsOutSQMs1 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSQMs1$OOB_RMSE
resultsOutSQMs1$OOB_MAE
resultsOutSQMs1$Rsquared



<!-- rnb-output-end -->

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```r
```r

resultsOutSQMs1 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSQMs1$OOB_RMSE
resultsOutSQMs1$OOB_MAE
resultsOutSQMs1$Rsquared

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->



<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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# store results
resdHiAnnoyFitAB['Abs SQMs inc tonal loud', 'RMSE'] <- resultsOutSQMs1$OOB_RMSE
resdHiAnnoyFitAB['Abs SQMs inc tonal loud', 'MAE'] <- resultsOutSQMs1$OOB_MAE
resdHiAnnoyFitAB['Abs SQMs inc tonal loud', 'Rsquared'] <- resultsOutSQMs1$Rsquared
resdHiAnnoyPermImpAB$AbsSQMs1 <- resultsOutSQMs1$conditional_permimp



<!-- rnb-output-end -->

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```r
```r

# store results
resdHiAnnoyFitAB['Abs SQMs inc tonal loud', 'RMSE'] <- resultsOutSQMs1$OOB_RMSE
resdHiAnnoyFitAB['Abs SQMs inc tonal loud', 'MAE'] <- resultsOutSQMs1$OOB_MAE
resdHiAnnoyFitAB['Abs SQMs inc tonal loud', 'Rsquared'] <- resultsOutSQMs1$Rsquared
resdHiAnnoyPermImpAB$AbsSQMs1 <- resultsOutSQMs1$conditional_permimp

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


###### Plot results


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutSQMs1.conimp <- arrange(resultsOutSQMs1$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutSQMs1.conimp) + geom_col(aes(x=factor(rownames(resultsOutSQMs1.conimp), levels=rownames(resultsOutSQMs1.conimp)), y=CondPermImp), fill=mycolours[7], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (% highly annoyed)\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 90))
pBar

if (saveplots){
  ggsave(filename=\PtsABdHiAnnoyAbsSQMsTonLdConPermimp.svg\, width=8, height=2.4, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdHiAnnoyAbsSQMsTonLdConPermimp.svg\)
  
  ggsave(filename=\PtsABdHiAnnoyAbsSQMsTonLdConPermimp.pdf\, width=8, height=2.4, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdHiAnnoyAbsSQMsTonLdConPermimp.pdf\)
}



<!-- rnb-output-end -->

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```r
```r
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutSQMs1.conimp <- arrange(resultsOutSQMs1$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutSQMs1.conimp) + geom_col(aes(x=factor(rownames(resultsOutSQMs1.conimp), levels=rownames(resultsOutSQMs1.conimp)), y=CondPermImp), fill=mycolours[7], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (% highly annoyed)\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 90))
pBar

if (saveplots){
  ggsave(filename=\PtsABdHiAnnoyAbsSQMsTonLdConPermimp.svg\, width=8, height=2.4, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdHiAnnoyAbsSQMsTonLdConPermimp.svg\)
  
  ggsave(filename=\PtsABdHiAnnoyAbsSQMsTonLdConPermimp.pdf\, width=8, height=2.4, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdHiAnnoyAbsSQMsTonLdConPermimp.pdf\)
}

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


##### Exclude tonal loudness

###### Set variables


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->

iVars <- c(absVar, eventVar, ambVar, sharpVar, tonalVar, fluctVar, roughVar, impulsVar)
dVar <- \dHighAnnoyPc\

seeds <- c(546, 57203, 270835, 60592, 8094)



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


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```r
```r

iVars <- c(absVar, eventVar, ambVar, sharpVar, tonalVar, fluctVar, roughVar, impulsVar)
dVar <- \dHighAnnoyPc\

seeds <- c(546, 57203, 270835, 60592, 8094)

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


###### Hyperparameter tuning


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p



<!-- rnb-output-end -->

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```r
```r

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Selected hyperparameters


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVYRzV1ZEhKbFpTQThMU0F4TlRBeFhHNXRkSEo1SUR3dElHRnpMbWx1ZEdWblpYSW9iR1Z1WjNSb0tHbFdZWEp6S1M4eExqSTFLVnh1WEc1Z1lHQWlmUT09IC0tPlxuXG5gYGByXG5cbm50cmVlIDwtIDE1MDFcbm10cnkgPC0gYXMuaW50ZWdlcihsZW5ndGgoaVZhcnMpLzEuMjUpXG5cbmBgYFxuXG48IS0tIHJuYi1zb3VyY2UtZW5kIC0tPlxuIn0= -->

ntree <- 1501
mtry <- as.integer(length(iVars)/1.25)



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dVhHNXVkSEpsWlNBOExTQXhOVEF4WEc1dGRISjVJRHd0SUdGekxtbHVkR1ZuWlhJb2JHVnVaM1JvS0dsV1lYSnpLUzh4TGpJMUtWeHVYRzVnWUdCY2JtQmdZQ0o5IC0tPlxuXG5gYGByXG5gYGByXG5cbm50cmVlIDwtIDE1MDFcbm10cnkgPC0gYXMuaW50ZWdlcihsZW5ndGgoaVZhcnMpLzEuMjUpXG5cbmBgYFxuYGBgXG5cbjwhLS0gcm5iLXNvdXJjZS1lbmQgLS0+XG4ifQ== -->


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```r
```r

ntree <- 1501
mtry <- as.integer(length(iVars)/1.25)

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->



###### Run model

Train preliminary model


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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nperm <- 5

resultsOutSQMs2 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSQMs2$OOB_RMSE
resultsOutSQMs2$OOB_MAE
resultsOutSQMs2$Rsquared



<!-- rnb-output-end -->

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```r
```r

nperm <- 5

resultsOutSQMs2 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSQMs2$OOB_RMSE
resultsOutSQMs2$OOB_MAE
resultsOutSQMs2$Rsquared

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Train multiple seeds model


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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resultsOutSQMs2 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSQMs2$OOB_RMSE
resultsOutSQMs2$OOB_MAE
resultsOutSQMs2$Rsquared



<!-- rnb-output-end -->

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```r
```r

resultsOutSQMs2 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSQMs2$OOB_RMSE
resultsOutSQMs2$OOB_MAE
resultsOutSQMs2$Rsquared

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->



<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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# store results
resdHiAnnoyFitAB['Abs SQMs no tonal loud', 'RMSE'] <- resultsOutSQMs2$OOB_RMSE
resdHiAnnoyFitAB['Abs SQMs no tonal loud', 'MAE'] <- resultsOutSQMs2$OOB_MAE
resdHiAnnoyFitAB['Abs SQMs no tonal loud', 'Rsquared'] <- resultsOutSQMs2$Rsquared
resdHiAnnoyPermImpAB$AbsSQMs2 <- resultsOutSQMs2$conditional_permimp



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


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```r
```r

# store results
resdHiAnnoyFitAB['Abs SQMs no tonal loud', 'RMSE'] <- resultsOutSQMs2$OOB_RMSE
resdHiAnnoyFitAB['Abs SQMs no tonal loud', 'MAE'] <- resultsOutSQMs2$OOB_MAE
resdHiAnnoyFitAB['Abs SQMs no tonal loud', 'Rsquared'] <- resultsOutSQMs2$Rsquared
resdHiAnnoyPermImpAB$AbsSQMs2 <- resultsOutSQMs2$conditional_permimp

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


###### Plot results


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutSQMs2.conimp <- arrange(resultsOutSQMs2$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutSQMs2.conimp) + geom_col(aes(x=factor(rownames(resultsOutSQMs2.conimp), levels=rownames(resultsOutSQMs2.conimp)), y=CondPermImp), fill=mycolours[7], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (% highly annoyed)\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 90))
pBar

if (saveplots){
  ggsave(filename=\PtsABdHiAnnoyAbsSQMsNoTonLdConPermimp.svg\, width=8, height=2.4, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdHiAnnoyAbsSQMsNoTonLdConPermimp.svg\)
  
  ggsave(filename=\PtsABdHiAnnoyAbsSQMsNoTonLdConPermimp.pdf\, width=8, height=2.4, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdHiAnnoyAbsSQMsNoTonLdConPermimp.pdf\)
}



<!-- rnb-output-end -->

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```r
```r
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutSQMs2.conimp <- arrange(resultsOutSQMs2$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutSQMs2.conimp) + geom_col(aes(x=factor(rownames(resultsOutSQMs2.conimp), levels=rownames(resultsOutSQMs2.conimp)), y=CondPermImp), fill=mycolours[7], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (% highly annoyed)\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 90))
pBar

if (saveplots){
  ggsave(filename=\PtsABdHiAnnoyAbsSQMsNoTonLdConPermimp.svg\, width=8, height=2.4, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdHiAnnoyAbsSQMsNoTonLdConPermimp.svg\)
  
  ggsave(filename=\PtsABdHiAnnoyAbsSQMsNoTonLdConPermimp.pdf\, width=8, height=2.4, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdHiAnnoyAbsSQMsNoTonLdConPermimp.pdf\)
}

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


### Difference variables

Next, the difference metrics are analysed

#### Set variables


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->

iVars <- c(names(stimDataNum)[which(colnames(stimDataNum)==\LAeqLAF90diff\):
                               which(colnames(stimDataNum)==\dImpulsSHM05ExMaxLR\)],
           \UASEvents\, \SNRlevel\, \IntermitRatioC2MaxLR\, \IntermitRatioC3MaxLR\, \IntermitRatioC5MaxLR\)
dVar <- \dHighAnnoyPc\

seeds <- c(568392, 498, 4089, 78132, 741809)



<!-- rnb-output-end -->

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<!-- rnb-source-begin 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 -->

```r
```r

iVars <- c(names(stimDataNum)[which(colnames(stimDataNum)==\LAeqLAF90diff\):
                               which(colnames(stimDataNum)==\dImpulsSHM05ExMaxLR\)],
           \UASEvents\, \SNRlevel\, \IntermitRatioC2MaxLR\, \IntermitRatioC3MaxLR\, \IntermitRatioC5MaxLR\)
dVar <- \dHighAnnoyPc\

seeds <- c(568392, 498, 4089, 78132, 741809)

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


#### Hyperparameter tuning


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5wIDwtIG10cnlUdW5lKGRhdGFJbj1zdGltRGF0YU51bSwgaVZhcnM9aVZhcnMsIGRWYXI9ZFZhciwgc2VlZD1zZWVkc1sxOjJdLFxuICAgICAgICAgICAgIG50cmVlcz1udHJlZXMsIG1pbnNwbGl0PW1pbnNwbGl0LCBtaW5idWNrZXQ9bWluYnVja2V0KVxuYGBgXG5gYGAifQ== -->

```r
```r

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

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\" 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" />

<!-- rnb-plot-end -->


<!-- rnb-output-end -->

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<!-- rnb-text-begin -->


Selected hyperparameters


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVYRzV1ZEhKbFpTQThMU0EwTURBeFhHNXRkSEo1SUR3dElHRnpMbWx1ZEdWblpYSW9iR1Z1WjNSb0tHbFdZWEp6S1M4eUxqSTFLVnh1WEc1Z1lHQWlmUT09IC0tPlxuXG5gYGByXG5cbm50cmVlIDwtIDQwMDFcbm10cnkgPC0gYXMuaW50ZWdlcihsZW5ndGgoaVZhcnMpLzIuMjUpXG5cbmBgYFxuXG48IS0tIHJuYi1zb3VyY2UtZW5kIC0tPlxuIn0= -->

ntree <- 4001
mtry <- as.integer(length(iVars)/2.25)



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dVhHNXVkSEpsWlNBOExTQTBNREF4WEc1dGRISjVJRHd0SUdGekxtbHVkR1ZuWlhJb2JHVnVaM1JvS0dsV1lYSnpLUzh5TGpJMUtWeHVYRzVnWUdCY2JtQmdZQ0o5IC0tPlxuXG5gYGByXG5gYGByXG5cbm50cmVlIDwtIDQwMDFcbm10cnkgPC0gYXMuaW50ZWdlcihsZW5ndGgoaVZhcnMpLzIuMjUpXG5cbmBgYFxuYGBgXG5cbjwhLS0gcm5iLXNvdXJjZS1lbmQgLS0+XG4ifQ== -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5udHJlZSA8LSA0MDAxXG5tdHJ5IDwtIGFzLmludGVnZXIobGVuZ3RoKGlWYXJzKS8yLjI1KVxuXG5gYGBcbmBgYCJ9 -->

```r
```r

ntree <- 4001
mtry <- as.integer(length(iVars)/2.25)

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


#### Run model

Train preliminary model


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->

nperm <- 5

resultsOutDiffs <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutDiffs$OOB_RMSE



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5ucGVybSA8LSA1XG5cbnJlc3VsdHNPdXREaWZmcyA8LSBjcmZSZWcoZGF0YUluPXN0aW1EYXRhTnVtLCBpVmFycz1pVmFycywgZFZhcj1kVmFyLCBzZWVkcz1zZWVkc1sxOjJdLCBudHJlZT1udHJlZSwgbXRyeT1tdHJ5LCBwZXJtSW1wQ29uZFRocmVzPXBlcm1JbXBDb25kVGhyZXMsIG5wZXJtPW5wZXJtLCBtaW5zcGxpdD1taW5zcGxpdCwgbWluYnVja2V0PW1pbmJ1Y2tldClcblxuIyBwcmludCBtb2RlbCBwcmVkaWN0aW9uIHJlc3VsdHNcbnJlc3VsdHNPdXREaWZmcyRPT0JfUk1TRVxuYGBgXG5gYGAifQ== -->

```r
```r

nperm <- 5

resultsOutDiffs <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutDiffs$OOB_RMSE
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDYuNDc4MDU4XG5cbjwhLS0gcm5iLXNvdXJjZS1iZWdpbiBleUprWVhSaElqb2lZR0JnY2x4dWNtVnpkV3gwYzA5MWRFUnBabVp6SkU5UFFsOU5RVVZjYm1CZ1lDSjkgLS0+XG5cbmBgYHJcbnJlc3VsdHNPdXREaWZmcyRPT0JfTUFFXG5gYGBcblxuPCEtLSBybmItc291cmNlLWVuZCAtLT5cbiJ9 -->

[1] 6.478058

resultsOutDiffs$OOB_MAE



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dWNtVnpkV3gwYzA5MWRFUnBabVp6SkU5UFFsOU5RVVZjYm1CZ1lGeHVZR0JnSW4wPSAtLT5cblxuYGBgclxuYGBgclxucmVzdWx0c091dERpZmZzJE9PQl9NQUVcbmBgYFxuYGBgXG5cbjwhLS0gcm5iLXNvdXJjZS1lbmQgLS0+XG4ifQ== -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucmVzdWx0c091dERpZmZzJE9PQl9NQUVcbmBgYFxuYGBgIn0= -->

```r
```r
resultsOutDiffs$OOB_MAE
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDQuOTU5ODY3XG5cbjwhLS0gcm5iLXNvdXJjZS1iZWdpbiBleUprWVhSaElqb2lZR0JnY2x4dWNtVnpkV3gwYzA5MWRFUnBabVp6SkZKemNYVmhjbVZrWEc1Z1lHQWlmUT09IC0tPlxuXG5gYGByXG5yZXN1bHRzT3V0RGlmZnMkUnNxdWFyZWRcbmBgYFxuXG48IS0tIHJuYi1zb3VyY2UtZW5kIC0tPlxuIn0= -->

[1] 4.959867

resultsOutDiffs$Rsquared



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dWNtVnpkV3gwYzA5MWRFUnBabVp6SkZKemNYVmhjbVZrWEc1Z1lHQmNibUJnWUNKOSAtLT5cblxuYGBgclxuYGBgclxucmVzdWx0c091dERpZmZzJFJzcXVhcmVkXG5gYGBcbmBgYFxuXG48IS0tIHJuYi1zb3VyY2UtZW5kIC0tPlxuIn0= -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucmVzdWx0c091dERpZmZzJFJzcXVhcmVkXG5gYGBcbmBgYCJ9 -->

```r
```r
resultsOutDiffs$Rsquared
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDAuNjQxMjMwNlxuIn0= -->

```
[1] 0.6412306
```



<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Train multiple seeds model


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->

resultsOutDiffs <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutDiffs$OOB_RMSE



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5yZXN1bHRzT3V0RGlmZnMgPC0gbXVsdGlfY3JmUmVnKGRhdGFJbj1zdGltRGF0YU51bSwgaVZhcnM9aVZhcnMsIGRWYXI9ZFZhciwgc2VlZHM9c2VlZHMsIG50cmVlPW50cmVlLCBtdHJ5PW10cnksIHBlcm1JbXBDb25kVGhyZXM9cGVybUltcENvbmRUaHJlcywgbnBlcm09bnBlcm0sIG1pbnNwbGl0PW1pbnNwbGl0LCBtaW5idWNrZXQ9bWluYnVja2V0KVxuXG4jIHByaW50IG1vZGVsIHByZWRpY3Rpb24gcmVzdWx0c1xucmVzdWx0c091dERpZmZzJE9PQl9STVNFXG5gYGBcbmBgYCJ9 -->

```r
```r

resultsOutDiffs <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutDiffs$OOB_RMSE
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDYuNDkyMjFcblxuPCEtLSBybmItc291cmNlLWJlZ2luIGV5SmtZWFJoSWpvaVlHQmdjbHh1Y21WemRXeDBjMDkxZEVScFptWnpKRTlQUWw5TlFVVmNibUJnWUNKOSAtLT5cblxuYGBgclxucmVzdWx0c091dERpZmZzJE9PQl9NQUVcbmBgYFxuXG48IS0tIHJuYi1zb3VyY2UtZW5kIC0tPlxuIn0= -->

[1] 6.49221

resultsOutDiffs$OOB_MAE



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dWNtVnpkV3gwYzA5MWRFUnBabVp6SkU5UFFsOU5RVVZjYm1CZ1lGeHVZR0JnSW4wPSAtLT5cblxuYGBgclxuYGBgclxucmVzdWx0c091dERpZmZzJE9PQl9NQUVcbmBgYFxuYGBgXG5cbjwhLS0gcm5iLXNvdXJjZS1lbmQgLS0+XG4ifQ== -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucmVzdWx0c091dERpZmZzJE9PQl9NQUVcbmBgYFxuYGBgIn0= -->

```r
```r
resultsOutDiffs$OOB_MAE
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDQuOTY4NjkyXG5cbjwhLS0gcm5iLXNvdXJjZS1iZWdpbiBleUprWVhSaElqb2lZR0JnY2x4dWNtVnpkV3gwYzA5MWRFUnBabVp6SkZKemNYVmhjbVZrWEc1Z1lHQWlmUT09IC0tPlxuXG5gYGByXG5yZXN1bHRzT3V0RGlmZnMkUnNxdWFyZWRcbmBgYFxuXG48IS0tIHJuYi1zb3VyY2UtZW5kIC0tPlxuIn0= -->

[1] 4.968692

resultsOutDiffs$Rsquared



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dWNtVnpkV3gwYzA5MWRFUnBabVp6SkZKemNYVmhjbVZrWEc1Z1lHQmNibUJnWUNKOSAtLT5cblxuYGBgclxuYGBgclxucmVzdWx0c091dERpZmZzJFJzcXVhcmVkXG5gYGBcbmBgYFxuXG48IS0tIHJuYi1zb3VyY2UtZW5kIC0tPlxuIn0= -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucmVzdWx0c091dERpZmZzJFJzcXVhcmVkXG5gYGBcbmBgYCJ9 -->

```r
```r
resultsOutDiffs$Rsquared
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDAuNjM5NTA0MlxuIn0= -->

```
[1] 0.6395042
```



<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->



<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->

# store results
resdHiAnnoyFitAB['Diff vars', 'RMSE'] <- resultsOutDiffs$OOB_RMSE
resdHiAnnoyFitAB['Diff vars', 'MAE'] <- resultsOutDiffs$OOB_MAE
resdHiAnnoyFitAB['Diff vars', 'Rsquared'] <- resultsOutDiffs$Rsquared
resdHiAnnoyPermImpAB$DiffVars <- resultsOutDiffs$conditional_permimp



<!-- rnb-output-end -->

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```r
```r

# store results
resdHiAnnoyFitAB['Diff vars', 'RMSE'] <- resultsOutDiffs$OOB_RMSE
resdHiAnnoyFitAB['Diff vars', 'MAE'] <- resultsOutDiffs$OOB_MAE
resdHiAnnoyFitAB['Diff vars', 'Rsquared'] <- resultsOutDiffs$Rsquared
resdHiAnnoyPermImpAB$DiffVars <- resultsOutDiffs$conditional_permimp

```
```

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#### Plot results


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par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutDiffs.conimp <- arrange(resultsOutDiffs$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutDiffs.conimp) + geom_col(aes(x=factor(rownames(resultsOutDiffs.conimp), levels=rownames(resultsOutDiffs.conimp)), y=CondPermImp), fill=mycolours[8], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (% highly annoyed)\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar



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```r
```r
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutDiffs.conimp <- arrange(resultsOutDiffs$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutDiffs.conimp) + geom_col(aes(x=factor(rownames(resultsOutDiffs.conimp), levels=rownames(resultsOutDiffs.conimp)), y=CondPermImp), fill=mycolours[8], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (% highly annoyed)\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar
```
```

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```

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<img src=\data:image/png;base64
```



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\" 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/>

<!-- rnb-plot-end -->


<!-- rnb-output-end -->

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if (saveplots){
  ggsave(filename=\PtsABdHiAnnoyDiffVarsConPermimp.svg\, width=8, height=10, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdHiAnnoyDiffVarsConPermimp.svg\)
  
  ggsave(filename=\PtsABdHiAnnoyDiffVarsConPermimp.pdf\, width=8, height=10, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdHiAnnoyDiffVarsConPermimp.pdf\)
}



<!-- rnb-output-end -->

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```r
```r

if (saveplots){
  ggsave(filename=\PtsABdHiAnnoyDiffVarsConPermimp.svg\, width=8, height=10, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdHiAnnoyDiffVarsConPermimp.svg\)
  
  ggsave(filename=\PtsABdHiAnnoyDiffVarsConPermimp.pdf\, width=8, height=10, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdHiAnnoyDiffVarsConPermimp.pdf\)
}

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

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<!-- rnb-chunk-begin -->


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# Plot only positive values
resultsOutDiffs.conimpPtv <- resultsOutDiffs.conimp |>
                                        rownames_to_column('Metric') |>
                                            filter_if(is.numeric, all_vars(. > 0)) |>
                                                  column_to_rownames('Metric')

pBar <- ggplot(resultsOutDiffs.conimpPtv) + geom_col(aes(x=factor(rownames(resultsOutDiffs.conimpPtv), levels=rownames(resultsOutDiffs.conimpPtv)), y=CondPermImp), fill=mycolours[8], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (% highly annoyed)\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar



<!-- rnb-output-end -->

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```r
```r

# Plot only positive values
resultsOutDiffs.conimpPtv <- resultsOutDiffs.conimp |>
                                        rownames_to_column('Metric') |>
                                            filter_if(is.numeric, all_vars(. > 0)) |>
                                                  column_to_rownames('Metric')

pBar <- ggplot(resultsOutDiffs.conimpPtv) + geom_col(aes(x=factor(rownames(resultsOutDiffs.conimpPtv), levels=rownames(resultsOutDiffs.conimpPtv)), y=CondPermImp), fill=mycolours[8], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (% highly annoyed)\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar
```
```

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```

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<img src=\data:image/png;base64
```



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\" 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ZBmE92rlWhGKiZdm+mTZNhdcwcavArNXIQ5moWdNeEk2QtLyYLY+fiCt3EnnGlqFXM0VDvCjWiJ7ppwWlUHo0oayy4FJ66JGwtn+xSgCgjN2KcnNMsJ5vGYpyc0ywnm8Zin5yzNBPZBaCaoARbSTJYz7EM5muGafjvxujVO7bN/NXL7Tje91/cOaJ+hOvJs6nDEUgfXQGjmDEqOZtS7UL8A4+92mmUgl2udfPYGaEhj3Ge+d7D98HLicI10B9dFdimYphnza2zm6VU6BaBOrGj0ffX8bU8becFSQqo56eihTk78CXtoqgfdyBV3b33qdfyNEXVwHa4PhtAqDMa6XNI5P/uu2IHNSefhcDS51CfjL1II7b6kmqOOrnXyYQ8IuHEnQTN1eLj7zdHsC93BFRVOjIAfI6MZ+/SqHc2wEytV9QTdkmiGdl8kCenooU7ejtTLQNMMD8fd5x/cubV7f6Q7uKptt8bQ/DBvDY2iiWeA+e/IPL3KT5oBGn0XNJvukt0XVXPU0UOdfLMXieS70UlTDX6we+Bf/LKvzqrUwTWY/ugKaO2J7ATWo/RME2489D2v9eHGCI2/HungeAMZSeeR2xdmmtSYFW4oG0JVFzyc3MGzj4P76hRKHVzHiroB7h7LTmA9OK6bfTPCjtWB0MwZcKSZr00oQjNnwJFmC4h0zj49Z6VzJ773xsIJzZZx4oKGiQ9h/jsyT682mpEJuJfePN3V2mfrazVzzDYAr1zieJW8QzD/HZmnVxfNyAQ8SUvnc1gMQ7fS3oPe/P1BpgF45RLHK+QtqBGr0kybgGFpdaFowoDURlMvGurQHpdpAF65xLFJLVIe+dqBFybgmKKJ/0zPj8J3QK3MNACvXOK4dN6CWrEqzbQJGArKLhRNXM9HtyWNZsNeEGQagFcucVw6b0GtMDCaoQkYHiNF8zZcwWuaqWdwQr2caQCWEse1h7NzChCagOd9dXkVKpooSoKp9yzYg9Hx2wkyDcDStrXucHbSLBMLUXJFCM3Yp9cgzRai5IoQmrFPz1kVQGAfhGaCGmAhzWQ5wz6UohmKj3BbLJkroYbxNa1UxisUo4qJmFDpKVwNg0Nwtrl+dVVNs1jWguZQ8iZtWEeFXmBoroxqGCcdvXqlDBBKmQA8hHTNVTXNglkfAebX2MzTq3IKENEMzZVRDWOqyjjdPPsFVDfGdf/Ja6BsaikTj8VDSAlYVdNk6asUn6YxTTOkGZkrqYYxbJpu+dD+9xuoLYsq5k+gBUCHpEztnyM/JumaK2qaBbM+AsyHC+bpVT6a3YzMlWENYzV6zffGcLEF1KHRrENVjlHKhD3pED2arahpFsz6CDD/HZmnVyXN5u8PkBNkroxqGKtrM0Wz+yM1qPX0tRncAtQJYjZNOoR0zXKapsA+lFzQmG6SOonmyt9cWNwTO/F6X3epujGqmBMPJ6V0DxpcguEhr7zlSdvW04Sq180my2V+CkBo5ggqptliUawUhGaOwEIVwIlr4sbC2TQFqAtCM/bpOUszWdCwL15JmpUpPjsG7XNgsvZs0ayzwfx3ZJ5etaNZmeKzoH1eM1l7tnDWgoawAs0KF58lQdxg7dnm/YfymO9x1dGsSPHZEWifJmvPFs5a0BBK06xE8dkAeWiw9mzZP1pQN1Y5aRYtPjuJ9PGVas86cU3cWDirpgBlis8G0L91tHrtWSe+98bCWUWzSnFC7VknvvfGwgnNQpxQe9aJ772xcEKzZYh07giEZoIaYCHNZDHDPtTi0wRZc2bYp1ksY0GzqMenCetkhn2aBTM+BsyvsZmnx8inmaCZIZ+mOc3t0LCGZzjeoam8moxXi08zTjNTPs2CGR8D5sMF8/Q4+TRjNDPl0yyY8TFg/jsyT4+TTzOkmfg0Tylq8WnCTgfi0zzFEJ+moAaIT1NQAyxUAZy4Jm4snE1TgLogNGOfntAsJ5jHY56eszQzLWky/x2Zp8eNZsfo7Sdkl4Io5/ahxtHsCL39cYpm2E0skV0KQjP70ATNEnq7Ypn2AK8PsNPrxOuA+H6CdM7E5iqP+R4boFlcb9cdXBXZ7g0mr/c3RvuDB1sBiO94iIxmjqBumi3p7XDOBA/wEPpWQwtOUNrDRV2ZArBPj9sU4Ei9fQs9wHA2DWkG4nssuxSc+N4bC+c8zY7Q22Fku4l3oGGn1/as2wHxPZZdCk58742Fc59mxSE0Y5+eszQT2AehmaAGCM0ENcBCmsmymX1YlWb7V2Pu3QWmu4H2/MJ92xu//mTh/EVFk1bGfLgdO5Q10QbcSfiJ66EZ82ts5unVMgUAXXLh3o0wh8J42vM7vTiedTsL5y+VMgYTCnpSFrKmCkTl9dLZpSA0sy/eSjSjOsbo3l0UN4ZRqY3qJK3x3yMOaeevVjSpyLFeoX08mKMZeEc3qsODj9c0TWpuh4Y1PMPxDk3l1WS8lWhGdYzRvbsoboz/TM+P8B11OryJA1Tk/NWKJtAM/MK7oaz5iRY4/dZDlDbxA2Q0Y59eDaMZ1TFG9+6iuDHW9kTZiEYzYkzo/A0VzeRohg4oGM36vfkeHRzLLgWZAtiHVUczv3WA7t1FcePbMB8Iaaa4ByRUbNLO37CUceLaTO0EZuCdIBjmoJnAPqxEM13HGNy716LixligGNTJ72jFUo1f7Zl2/q5f0KWMh+QSppmmd6mvzcCt8cTrobQZy05gPcyvmy0KFK8OoZkjME8z//jb+wtBpHP26TkrnTvxvTcWTmi2DKEZ+/ScpZnp9QzmvyPz9BqnGbV2nW6qKaXv4SxSbSJpk14FGb1a9wezN0e4QyeoiWaC6lHlaEbtDFHfvD/SlRjDRq7xescxEO2muyC9B0IzZ1AxzaA56y/7vQXNtLSZrHcMY9zQe/UKaaTbB3SvxjGaplENUh7t9mnq0ezy7I1BRDMtbSbqHd8bYI1av4caqd8a40uAjGaOoFKaYXPW+eVgevFWRDNd1Dhe7xgsmvsDRTPUSOd7NdOM+TU28/QanwLou8dAEvDxriC4H0NLm4l6x2DR9PVo5rcenkQzJ773xsI5RzNqzqqGNEWZm3e6unwxSpuIsN4xVD9uR71eexPvUj+8IVdoxj69xmlWFP7Srd5CM/PhTjvNppvL1ZBFOncEfGiWBaGZIxCaCWqAhTSTdTP70JBPk1q6ThZNeIr5NEunmwXm19jM02Pt06SWrp9HO8FBBXyaZdPNBPPfkXl6rH2aZGrqhg1ci/o0jWpuh4Y1PMPxDk3l1WS8hnyaaNH83XiCNpXiPs1y6R4B5sMF8/RY+zRxNAuCaKeCPs1y6QoaQ0M+Tbo20ydJ8Wk6j+Z8moqaVIpWfJruQ3yaghogPk3u8ZinZ5t0ngWhGfv0hGY5wTwe8/ScpZmsm9kXrxqaZVg04Y5/76z4NE8nKhrNli2a872D7YeXxad5OlEdzdIWTRTJxad5Oh+rHc3iFk2imfg0TyWqotmyRVPTTHyazYZzaQqQZdHUNBOfZrPhXKJZhkUTrr46M/FpNh3OJZqVgvg06wh32mkmPk2HwYdmWRCaOQKhmaAGWEgzWTezD0Iz7tfYzNOragow63qtse+Fxt5Zt/XZNe3pjVt50e8LAA8wHgMvElL5bOERXkQm2b0Xzy4FoZl98UqMZriCfz809vo9sATDamviZgvtKQHAUm22VL7wCMciBzF/sdCMfXrV0wx1b2QEbsKmhdPNs1+o8aiDDrnJa+DrjdMsKZWHQroPS7VgCI5o1g7EDqwfD03l1WS8FWimG7BOoPAFbJpu+a3xvcE3I1hnBb/vT9Tp0O/EaZaSyiOPcDDpoSGYaIb+YoRMARxBWZotGrDCeVOPZvO9MVxpAW9oNOvAyTNGs5RUHgnpXQ/tweFoBtbOWHYpCM3sQwmazd8fICFI957oFr9wbaZodn+kBrWevjaDIWsxmi1L5aGQfnM06aEhmMogo784lp3AepRZ0Jhuko8Xde/fXFhUj5p4va+7qIC3QSiHwQubs7bwRsUlqfwdLaRvfOR5r7ylDcFQBhn8xYNFdgLrUem62aRz3LsZUnkaIp2zT6956XyxIpaFLKk8DaEZ+/Sap9nqEJqxT09olhPM4zFPz1mayXqGfahe04RCxnfMapoFshWwQA2aZljIGGBG0yySrYADqtc0w0LGeKwZTbN5e6s8FnusXtOkQsZGNc0i2Z4M5tfYzNOr9g6NApqmLmQMexrSNItkezKY/47M06uMZkU1zaiQsSFN04nvvbFw1tCsoKYJhYxnJjVNJ773xsLZQ7PcqEbTFNgHCzVNgX2wUAUQ2AehmaAGWEgzWdCwL14TNAMZ9L9F5Y9hJS0UCaI9tmsUm5j/jszT40szlEHD8scoYPqpvq9+nZom89+ReXp8aYYyaFj+mJq+7o1B6Byq/7UO1BP1uj5N89Cwhmc43qGpvJqM18i1GcigYfljFDAVrUDoxN6a8ARpFsgdGs6goSmA3wvLH6OAqWgFQmewP7iFT4RmjqGRkybIoGH5YxQwUQxV70x/dAUVz2NpJrAPTdDsMcmguvwxCJj/VQuhcOMHPJno22eFZo7AwnUzJ6ZejYU7PTPN/BCasU9PaJYTzOMxT09olhPM4zFPz1mayYKGfTBDs/2rcHts6yAlTeJttnCj/7zfeog3n/kwsaRbbcWneYpghGbg1NwJ5nufxqRJtWF6fhTM3hiA62R6BcucoUMAdPHHeA+2+DRPCwzQjJqt7swvQwfgbmeCA1hn4nXQOOJ3wKLy2zF1ctX+lMeDOfoyxad5Sh4N0AybreLqKrbChM6r6pz3YCsYwqkPzE+d2cdUUBvcm7tqVyiEAL5M8WnWHs7aKQA2W9WjGdEMiKPOj3o0C2Zv/t0gSI1m6EcRn2b94aylGTZbPdgJUIqc7gbDjY/IqAlVNeD6LBjinT/xa7PpFvoyxadZezhraaYtlx24wacXtl9tz7odmmkG2kg3pDq0NNP0LvXRlyk+zbrDMaHZo7UzT146YziVshDp3BGkafb0B09/Hjz9/o2m8klCaOYI0jR79ueKZo9e+KqpfJIQmjmCjJPm2trzTAYzoZkryJ4C/AMTnsmCBvv0SkwBnn53jVB0OEMdsp14nbLEoe4ZSphqBvo6eJlanw2GeKBP89SotFAyuxSEZvbFS45mv9LXZIVHs/kHo3k/rPAz21ny95LuqSXM2RsDcGKCXfPa3sH2w8tUqDasUftYaFZhOA40Kw1c4u9AKbPJq+dve1qdnPehmBmonKR7hhImLb6SPg57kjiga9RqvVN8mvrx0FReTcZbohmcOF8MiiIczSaX+q3xgy09min6+B1SOVH31BLmsAcE3BiBXRP3RKmTatRGeidC7tBwBEsLGu+eUbPNwsuzsLTfhkuyHtWbJZqpM+F0C2lGuqeWMP02DX/Iw2g0C3SNWq13xrJLQWhmH5aWZ/8ALs/KXJsFYH+bxGg23fXh7gxSOVH31BLmdLMHB0wiuVw3EQhr1KLeGctOYD2WTpp//57656+KzzTp3liv9SF0AngNa83CFRmeB0nqjErNwkxTjX1g1xxhjVr8x6cDtN5JE1WhmSPIXtBgrWk6MfVqLByHmaZe0HjyR4ZTKQuhGfv0Si5oXC+zPFsVhGbs0ytHs6c/ePbnwdOfG06lLIRm7NMrSbOfB4/OPftTzqOZLGjYh6WT5t3nb1zPuT67f60fFzIJYROwbuuzq12v9bDvvbwZ2TGTzRDFp3lqsMIdGrqPZgq6bGzUZlP9f2HHTLbTFJ/mqUFyQeP7N2hNI8cUgFTK7bH/tve214POmXdIvdzDnppRm02gmdYydTtNXWHW89ri0zwtj0uj2a9yqgCkUm4fdNuTtu6cibISlpH9ZkRtNrHkwTiyY1I7TRSUJnGRU3ya9YVjMgXIawMglXJ7DPLSdBclppBm+mqL2mzCaKa1zLADJ1SYRaOT+DTrD8eDZqQz5RvNfKjNEtHMJ/XyS+qpGbXZRJqRlqnbaWKF2Qlc5ItPs/5wPGiW+9oMToiX+t531DWV523c9kL1UvfUBMHyTjjTJC1z/QK10xxDhVmQNc9KP836w/GgWe5rsySO75yZAXX1BrW0T4BI544gc0GjsKZ5fOfMLPiJe/6PgtDMEVioaQrsg4WapsA+WKhpOnFN3Fg4JlOAAppm9ZB1M/bpMaikHbmCQTKn0sZX45u0SjCEVX90BMPS7ezNEe7aCYRmFYRzkGaRKxglc7L6JjdBYeQHvWDY064TuoVjujvdjWWXgtDMvnhJ6fwHhlMIOYWSOZnjkpuglCi6NNFDp+V4kNXVYCbSuTuPacuJ2aWM8OYzksy11Te5qQ3K0nSXuIbO4dZ40tHLvXIjkCNI0uznKDaZ8zVpTpFknhjN9CYYzZB+OJqhHI91koVmbiHr2uyusTGNtHAtmZPVN7lph3bbIUcwyvEPT6KZwD6kR7Pg2bvmRACcSa5f66Nk/spbYPV9J77p5U2YT/poJR7q+9N6E1Dk9W3asm7GPr2y9c3OGU5jFQjN2KdXhmZcShuHEJqxT4/DutmqEJqxT89Zmgnsg4U0k/UM+1CKZvu6MnECcNP/sgd4/UJ8PzN24GLJChigDM3CysTJrbpocdoDHIpLuERmxg5cKFkBBxSnGQqPyAiskjf9cXdjhCXw9sZZHmD1FHfBQuFkHVcAABqzSURBVLNm7MCGNbdD3vEOTeXVZLziNEPhMSwZ67+uTnlwa8Wkp2iW5QGebT/EXR5sBabswAWSzQPmUznm6VU100ThERmBFYynu2AL7npIswwPMIxmsAt0PDdkBy6QbB4w/x2Zp1cVzVB4HONopisY3xuAnxdOmhke4DjNxA7cfDhbaIbCI0qQeG023LjTbUNt4wdHeYDbuMus+3rfjB1YYB+aWjcTO/CpQlM0EzvwqYKFKoDAPlhIMyeuiRsLZ8sUoE7Iggb79Oqk2f5VXJM9SJWiVdPJdVpT67fGy61ZmWiazH9H5unVSDNQMsFx+WmsJTDc339+FMzewBWz6ZWs1qw8NE3mvyPz9GqjGbkrwaM036MmrWpA6ky8Di6y+p35+4Pgt+Os1qw8NE15rNmnWQ5U7BiESPS+gUNJnfMebAVDOPVNQCqYfZzZmpWHpimoHgZoRsWOaTQjmgFx1ElSj2bB7M2/G2S2ZuWhaQqqh5nRzG8dwO1k0BMAm7T2kGZwEyNcnwXDVnZr1nKapsA+GKCZbsmK88he2KS1Pet2aKYZ6Mq0y61ZQ91TShzXGM7aKUCFEJqxT09olhPM4zFPT2iWE8zjMU/PWZoJ7IOFNJMFDftghGYoQ7YTr1vj1D77VyMFU01AX987oH3I8Lmsd8azS0FoZh/MjGbzD0bzvpa7QSxabucKsmfo3HxjELoJQJ7afng5S++MZZeC0Mw+GKNZ4MOd/4PJq+dve1qcBK8AiZwke4YKJq29htVByYm3pHeKT1M/HprKq8l4ZkezyaV+a/xgS1MIfJzUy9WPuzKHPSDgxp0FzbL0ToTcocE+vTpnmrCy34ZLsh72b9U0Qx8n0oxkT61g+u1Icwpio1mQ1Dtj2aUgNLMvnrmTZhCANLmg2XQ39HEONz5C2VMrmNPNXiRt7uqKtMt6Zyy7Mn9XATCPxzy9GmmmhjG4Axbcmh9ujGbd17DA7AB9nKHICbInKZgw0wTNkyaVIHVm6J00U5V1M0dg4bqZwD4IzQQ1QGgmqAEW0syJa+LGwlk8BagMsqDBPr2KaJauO7vQL/evaUVy4m38+pOFBzPqnNlOhUpXosW+c1BoL5VdCkIz++IVpdly3dlQv9RlZx8PphfHs25n4cHEgrMJ1VMjXYl2R6+/pbNLQWhmX7xiNMuqOxvpl+t6Df8eWX21gqkLzpLqSSpn61Ov46thLl2Jdkcv0GJY8Wm69FiMZll1Z7V+qc2aioU3cUSKPJhUcJZGs1DlvDmafbFciRaP9lsPdTnbQO7QcAbFaJZZdzbUL0NFkigSKpi64CypnnjU/IM7t3bvj5Yr0arRrN8jE949oZlTKDyaLdWd1folqpakSF4co9GcFExdcJauuvCowL/4Zb+TUYl2JwiGOWgmsA/FaJZRd/YsaZPwDvbBBP84SZZh7VndORMut/AoNdAF99UpdLkSLRR6n3g9rFUbyy4FJ66JGwtnwxSgKhxViVZoxj49m2h2VCVaoRn79Gyi2VEQmrFPz1maCeyD0ExQAyykmayb2YdqaBbp6aiOQzfXwTHvx7E/mL05wnu1O4HQzBlUNJqFejqq49jN9Zj3Y6Adp7vT3Vh2KYh0bl+8immG6jjWxfbf9t72eslixvp9LNw+9F69ovX3A7olSOzAYgc+AaEJE9Vx6OY667YnbRij4sWM9fv3BhPQ0/0e6u9+a4wvATKasU+Pw2g2CTtJ+D2wcE53k8WM9ftDz2vvQxFk1N+xTrLQrKpwztFMjV2kjmM3V6JZspixfv/+iET5HurvD0+imcA+VDbT9OAqjNTx31yAklKtD71UMePw/YMuuYOpPvKEJPjeIjuB9WC0bub3ljYJzRwBH5pNNzdG6W1CM0fAh2ZZEOmcfXrOSudOfO+NhROaLUMWNNin1zTNsnXLxNYZ3Zc9BKMwVjluRNNk/jsyT69pmh2hWya2YrPXB71g2NOV9JrQNAXVowaaJXXL5FZoj4iVZ7EuaDOapjxyswMXQrZumdiKHVr9jqIZca0RTVNQPaqnWUq3TGzF0Qzd5jiaNaNpCqpHxTRb1i0TW8EBHOAlGl6b5dM0nbgmbiyca1OApK4Z6pbvxLe+vAnzSR+dwuAxzqdpOvG9NxbONZplIkO3PA5CM/bpcaRZlm55HIRm7NPjSLOiEOncEQjNBDXAQprJgoZ9qJRmCQEThcr1qxma5gSmms1pmoLqUe1olhQwsQDysqY5hn6tDWqazK+xmafHYQqQFDCxZOiypon9WhvUNA95xzs0lVeT8WqhWSRgrg+WNrUD7NfaoKbJfLhgnh6j0UwLmPHRLKZpBtCvtUFNk/nvyDw9LjRbCJhYADlL05x/MCqgaQrsQ9UzzYWA+cpbUAA5U9OEgqD5NU2BfbBw3UxgH4RmghpgIc2cuCZuLJyLU4BVITRjn56zNJMFDfvi5acZdGVtpzeGi63d1megVj7sq9kj9WuFaeWJjVqj1q76Nbba6YbzTKFZBeGY00x3ZU3BJx0cq8vC2+r/Yb/WPI1ao9au+jV+wGyHyogGIp07g5w0C7uyUgXZyWvdjTvdzmQdluynm2e/QFqENNP9WvM0atW7JivSAs3agbRtdekxJ82oK+v2AVaQ/Yk6zWH31X2g2b3BN9AbsxPe1hP2a83TqDVs7ZqoSItCJ0FGM0eQk2ZhV1Yq7ak4Mt0KaaavrcgaB6OZ7teap1Gr3jVZkXa2Ax05Y9mlIDSzD/lHM+zKGtHMh56tw40v98b3R4pywJKIZtSvNU+jVr1rsiKtOmn6nXh2KThxTdxYONZTAN2V9TtUQfY2nhxJgOx9rYaixxc8dbGmZ5rUr1UhR6NW3DVZkVbNWNUVHfU9FJqxT6/CdbNJ5+R9TsBRjVqTEJqxT686mi1WusrjqEatSQjN2KfnrAogsA9CM0ENEJoJasBqNJtnyJwI7AgWVyf1JsDE2/j1Ffj3TjlNU2AfVhzNQplT39SfeGsnpk7G3p9eHM+6nQDMmeU0TSeuiRsLZ+UUQBFj+uPuxghbGa4PJq+e/whVTxAM1CZUJ+Fef3ixPcZFtHs4wURzZjlN89Cw5sY83qGpvJqMtzLNHqpz3v7gwRZUl3293yLVc9dvjdWmUJ2c9NQLtQkFzps4eKE5s5ym6cR/3o2Fs3Y021UMm25hddlJJyA5Cu7c2NLqJCiW6oXahALnfrhcdm9QTtN04ntvLJztNIPqsks0A3WSFEugGQqcwCGQEeYfjMppmgL7sPpMc6hmjG11UQ/numHYN/M2NMd8jdTJP/bglrPOxOuRwDmBOzfggm1WTtMU2AdZNxPUAKGZoAZYSDMnrokbC2flFKBiCM3Ypyc0ywnm8ZinJzTLCebxmKdXDc2oUHHmDYm6SPFCDN+/2tWlihcljgcLCb2sHVhgHwqPZriESubxmB4eNveiV+Q2AYfwDj2PShynbOelpHOBfShFM7AxdUkP7+Iqa0c9xbKeYBQeIbXIQKxpFpY4DsgABQ5iOKqcdC6P9j2Wo9l0C9qwkl4O/ArwaaCNwgEZzsHvi1WNFzZgONJvoYMYjionnQvsQ9nRDIRyrZcDTdRTGs3AKIw0IwOxGs36vUWJYzhSO4jhKJHO6w9nxxQgvMK6T9zCbqw9fIrXZmgUDkczv3Wgrt6GvXiJ4x+CUR0cxDSaiR247nB20CyaL2Ib1g494FMsUjwBozAVLgh7tIJHOCpxrI7UDmL1vtiBGwhnB81OgAGjcBxCM/bpNUEzE0bhOGQK4AgsVAEE9kFoJqgBQjNBDShNs4IVjzdGKHlOvI0vI1W0pKbpxDVxY+HsmgIUrnhMkieagLF9K+wnduD6w9lEsxIVj1EkIBMw1J6do4YpdmCm9l3T8crRrETFY+zLSiZg3b71k5KaphP/eTcWzqbRrETFYxzNgoCOg3MmaJilNE2BfSg9mhWteKxvRwMTMLZvRQ1T7MCnBOVoVrzi8cYIJE8wAVOfnUt91DDFDnw6YGLdzLCQGYPQzBEYoJlpITMGmQKwT0+cTTnBPB7z9IRmOcE8HvP0hGY5wTwe8/QapRndZIs+zfZ0szUOsLsOKZYkJIGVTq/gDmE5Vrs89wezN7GrGL6UKYAjqGo0I+GSmh3OoS3Y/ZFefNVKKFk3d4L53oNeMOzpZTXycU53p7ux7ATWo0KaPR5o78n8l/3eEs3Iurkzv4zOu10UCbRUekBmdfFpuvNYGc1QuNSj2eXZG4MUzbR1E+RLv6NoRlwDwcpvjdGIB5DRzBFUPJph61Y1Yk0v3krSTFs3YTRDRuJohlLpfO8EmjlxTdxYOKemAGTK3KJiGtDR0N8YacUS/w2tm1SFI6zAgVLpQ6FZleGcohkJl+90oXWreq5Ic/NON5p8rl8j6+bLm/COj/1dh/reod4E1FJ9/6zQjH16sm6WE8zjMU/PWZoJ7IPQTFADhGaCGiA0E9QA0zRLaJZo2Bwc/X45TdOJa+LGwjkyBUholmjYPOb9UpqmE997Y+EcoxlqlmjYJDNn0ou5iqZ5aFhzYx7v0FReTcarimakWQZYBRkbuSa9mKtomk78591YOLdGs0m4kO/3yGWX9GKuomkK7EMlNIs0SzRsEs2SXsxVNE2BfahgprnQLFu/ueCFjVwTXsyVNE2BfZB1M0ENsJBmTlwTNxbOkSmAUQjN2KcnNMsJ5vGYpyc0ywnm8ZinVwfN8nTXhHqzcKu27q45X5SslX6apwYrjmZ5umt+jv9G3TVDtUm9kn6apwUGaHZCd02sNxvrrgn9NH+stk+kn+bpeTRBs+O7a2K92Vh3zdn2Q7V9f6B2kn6apwVmRrNju2tCvdlYd00YzXbVq+mW9NNsIJyNU4Bc3TXhTBnrrpmgmfTTrDucjTTL013T1zssumu2hxt3umqn16SfZv3hbKTZCVi5KK3QjH16zdNs9aK0MgVwBBaqAAL7IDQT1AChmaAGcKg9i6Kn+DTrCefWFKBA7dkxiJ7i06wpnHM0y1t7FkVP8Wke9XhoKq8m4zGoPYuip/g0awrn5GiWp/ZsAKKn+DSdBovas2o38Wk6DRa1Z0HAFJ+my5B1M0ENsJBmTlwTNxbOrSmAGQjN2KcnNMsJ5vGYpyc0ywnm8Zin5yzNBPahejtweHOj2IFPMWqwA9MardiBTzOqtwMjzcQOfLofq7cD69s0zNmBnbgmbiyclVOAPHZgoJnYgbmEs5ZmJ9mB9WgmdmAW4WykWR47MJQ6HosdmEs4G2l2AqqxAwvsg9iBBTVAVABBDRCaCWqAhTRz4pq4sXBuTAHKtG0VO3CN4dygWYm2rWIHrjOcYzTL37ZV7MD87Lum4zFo21rUDiywDwzatgZiB3YeLNq2ih3YdbBo2yp2YNch62bc4zFPj4F0vjKEZuzTE5rlBPN4zNMTmuUE83jM03OWZgL7UMlMM1nVGCwC3lntydSbEprmECwm+TVNgX0wPpotVzWe7x1sP7wcejKXNc0HvWDYK6BpCuxDFTRLVzXGJdvQk7msaZLOlF/TlEf7HqsazeJVjYlm2pO5rGn6HUUzKXFcUzhHpgAZVY01zbQnc0nTRJdwAU3Tie+9sXCu0Gy5qrGmGXkyszTNsOZGPk3Tie+9sXBu0Gy2XNV4AHrlTHsyEWlNEwxQ+TVNJ773xsK5QTOzkJmmIxCaCWqA0ExQA4RmghpgIc2cuCZuLJydU4D9ASiW7eh15MMM9q/pckETb+PXnyzKzJKehBLmBO6tDavXzmjtH50qyexScOJ7byyclTSb/vAaKpaLLaGYBLVmsXXr9OIYalJFZWZRzCQJ8/Mg7O+KR+7oRbd0dik48b03Fs5CmqEGiURRz1pUUpZoptVJaHZ4jzikJU0tZuKiPxo0cac5OjgTpWmPrj17aFhzYx7v0FReTcZbiWaoQSLNFG3817GkLNEsVmv2Jg5QUZlZEjNRwkSDZliQ9hPd6dVvUWla/ACZAjiClWiGGiTSTD2bbmGtT6IZ1ZrF0YwYE0qaWszE0SwIop3QwYmlaed7VDM0lp3Aeqw6mvlqPILRrB2AGVPTDO+18FsH2LoV6hWrE6SWNLWYqW8v020C1E7g4MTStEIzF7ESzVCDRDUSr82gpOxZkjHhHZQmwWruYUValDTXL5CBcwwHgUGT+rte6msHJ5QH6mFp2lh2Aush62bc4zFPz1np3InvvbFwQrNlCM3Ypyc0ywnm8Zin5yzNBPahKM3ydNAMsJhs1EEzIF9c1BElEi2ln+apQeHRLE8Hzc/x36iDZmjKXBwZHiX9NE8JStHshA6aqFXGOmiGpkx95PZ4unn2CzhK+mmelsdyNDu+gyZqlTFVMzRl0pE+FJr9ZgRHST/N+sNZMgXI00ETtMpYB83QlElHzvfGcDkGR0k/zfrDWUSzkzpowpky1kEzNGXSkYpm90dqUMPRTPpp1h3ODprl6aDp6x0WHTRBx3zlrfBm2t7XavRS75+Vfpr1h7ODZidg5Q6aSchM0xGYpdnqHTSTEJo5AlEBBDVAaCaoARbSzIlr4sbCOTEFMAyhGfv0GqRZRp3j2TEljqVta43hHKJZRp3jSCaXtq3NhnOMZuk6x0eXOC7atlUe7XusdDSL1zk+usSxtG11HxXRbLnO8dEljgNp2+o8qjtppuocH1fiWNq2uo6qZprpOsdXjytxXKxtqxPXxI2Fc2kKYApCM/bpCc1ygnk85ukJzXKCeTzm6TlLM4F9EJoJagAHTVPatjoPBpqmtG11Hww0zaJtWw8Na27M4x2ayqvJeAw0TWnbWmc4l2aahTRNadtaZzinaFZM05S2rfWFc4hmRTXNYm1bBfZB1s0ENUBoJqgBQjNBDbCQZk5cEzcWzqEpgDEIzdinVzHNStQ6Xi5pXKrEsRPfe2PhLKNZmVrHSyWNS5U4duJ7byycjTQrWOt4qaSxlDg+JY8r0qxYreOlksalShwL7MPKo1mRWsdLJY1LlTgW2IeVr82K1DpeKmlcqsSxwD6sPNMsUOv4YLmksZQ4rjucbVOA42Gm1rHQjH16jdLMUK1joRn79EQFyAnm8Zin5yzNBPZBaCaoAaZplmHRhDU176y52rMC+2B8NFu2aM73DrYfXjZXe1ZgH6qgWdqiiSu55mrPHhrW3JjHOzSVV5PxqhrN4hZNopmx2rNOTL0aC+fITDPDoqlpZqr2rBPfe2PhXKHZskVT08xU7VknvvfGwrlBswyL5gBEzpm52rMC+yDrZoIaIDQT1AChmaAGWEgzJ66JGwvnxhTALIRm7NNj4NMM7zoTnyafcJbRLJdPkxbRxKfJKJyNNDvBp4k0E5+mPFbr09Q6uvg0Tz2q9WkCzcSnKajYp6lHM/FpnnZU6tNUz1pj8WmyCmfbFOB4iE+TaTinaCY+Ta7hnKKZIQjN2KfnBM0EjgB/Tq40ywT36Sfz/BpMT2hmEMzzE5oJ3IbQTFADhGaCGiA0E9QAi2i26G3BELQqzTZFuFOryfTsoVnsnkmOgHsF+KZ4fzR7c9RgevbQDO8yajqJo4G3pHBOcX/QYHr20AxLujSdxNEAmrFO8da4wfTsoRnroYL/aDbpNZmePTTje+GD4H1thh4LuTbLgyHXaRxiSDdzMk3RR2tic+lZRDOBvRCaCWqA0ExQA4RmghogNBPUAKGZoAa4T7O7a2tn/v691MZnf/Le0+/hRv2QQNa2aOOTb3911C4nY/mwkoEwk99/+t21c+rJX93Al9+6EU8z8aoA1Ffz30undBScp9n1F74K7j6X/t6u05Yl+iXezcTT777w1Qm7pBB9yBGfluvYDCi+3z3zSGXzCKgWPHv3+RuLN/WrAmmGUIesQP0j4DrNnvwefGP0Q8Sg/pOFhyP+s9XvHhERR7Njd0ki+pASg8Qxhzz70xsB0uzpD2jD0+/HaKZfFUgziqsOefoHXxU97Hi4TrO7L+gvDM6d6rv/H2vwn//aGv4nqx7PwX+5sffUaej5G/jjPHt37UU1JvwNbHj6/Z8995ffew/ffPLtn669iD/G9TUMDgeq4/El7Pifv/3Xay/849oL//Td52+ooQE/BA7M+LQAT2tP9AFfhZH0Hj977j+Fx+rd76qn4cc+elFR/iV10vwHzS79B3zvPZX52vN/g6+e/clfYs4vwdgWixTFWzvzNNzhevTVqL8l/d/lijgtNFNj0JOX/r1iDvys37qhBjnFgP8LXHnuvfh7d88oAtEYAOPBP+OGX7yrfna1I7755KVzz949p548UtvOEB3V730OXv5btaN6rX66F+Hq7/tAWPgQPDDj0wI4R/0iPOAXOlK4x5kgPPYXtLti1vUz+mPVdvoLn/yR/lNVDLUPpIkfjq8oX33yXUTS8fCDzukdMC59NYvgpnBaaAbf2/Uz9MOr58QloBO9jt4D1pzTpxr1a/7qK9wAjAvfhJMm0kYNBYoW+vR09wy+hOcUSFFRP9FD47msT6PJSOwAjBTuEUTH0i44xoQfq5nw7M8UVZ7T/1nQxz1acBwC3I3Oq4tI0RcBfyTtgHHpqxGaFQVdmz35i7svwnd3BM1i7z1Sg0tIsyff+p9/EeAG/ZPj8wXN9IklJMc5/TyDZlHU1KcF2TQL9wiiY+M00x+rmaDGqevnHmm66z8LRsXFh6vANJOIRdJfBH6Q3gHjCs1KAmaa6opW0S36dh+pq693n//biGbx967rM2J0LG7QPzk+VzSjs5H6bXGUgJMfndGe/iBNs6ffhc8JoqipTwtSNKNI0R50BXguYixe74cfi9TCU2aaZo/OxF+pPeliLh6J/qUP0jtgXPpqbsi1WWFcX4PrZjgnnFPPX7yrvlK1ae0cPcGH2Hv/uPbcv37+Z/p3gd/wEWz4Q9yJ3vzbd2FvOhoXDdQ1NF02r9FltAqkHv4anz33b2CmcA6D3Fj+tK8grxd+Gh7wTzqS3gNIro/9wzDx594L9MfCTJOmhPqkSSFVmP/zkrqyj159FV3QxyL9lOLhBwV6h+s0v4BNMtPkh+RCQn2RaAE2A/+sSPerGE/U5dvxkdI7lFgGOQFCs5XRFM2CJ7+fuRkXvn4e27C0aphGegdRAfhBXXC/YOYUYyrSI7pM0LiO89JjcOIOBiA0E9QAoZmgBgjNBDVAaCaoAUIzQQ34/11HA1yzVnQ8AAAAAElFTkSuQmCC" />

<!-- rnb-plot-end -->


<!-- rnb-output-end -->

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if (saveplots){
  ggsave(filename=\PtsABdHiAnnoyDiffVarsConPermimpPtv.svg\, width=8, height=9, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdHiAnnoyDiffVarsConPermimpPtv.svg\)
  
  ggsave(filename=\PtsABdHiAnnoyDiffVarsConPermimpPtv.pdf\, width=8, height=9, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdHiAnnoyDiffVarsConPermimpPtv.pdf\)
}



<!-- rnb-output-end -->

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```r
```r

if (saveplots){
  ggsave(filename=\PtsABdHiAnnoyDiffVarsConPermimpPtv.svg\, width=8, height=9, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdHiAnnoyDiffVarsConPermimpPtv.svg\)
  
  ggsave(filename=\PtsABdHiAnnoyDiffVarsConPermimpPtv.pdf\, width=8, height=9, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdHiAnnoyDiffVarsConPermimpPtv.pdf\)
}

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->



<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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# Plot only values within 1% of the maximum
resultsOutDiffs.conimp1pc <- resultsOutDiffs.conimp |>
                                        rownames_to_column('Metric') |>
                                            filter_if(is.numeric, all_vars(. > max(resultsOutDiffs.conimp)/100)) |>
                                                  column_to_rownames('Metric')

pBar <- ggplot(resultsOutDiffs.conimp1pc) + geom_col(aes(x=factor(rownames(resultsOutDiffs.conimp1pc), levels=rownames(resultsOutDiffs.conimp1pc)), y=CondPermImp), fill=mycolours[8], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (% highly annoyed)\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar



<!-- rnb-output-end -->

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```r
```r

# Plot only values within 1% of the maximum
resultsOutDiffs.conimp1pc <- resultsOutDiffs.conimp |>
                                        rownames_to_column('Metric') |>
                                            filter_if(is.numeric, all_vars(. > max(resultsOutDiffs.conimp)/100)) |>
                                                  column_to_rownames('Metric')

pBar <- ggplot(resultsOutDiffs.conimp1pc) + geom_col(aes(x=factor(rownames(resultsOutDiffs.conimp1pc), levels=rownames(resultsOutDiffs.conimp1pc)), y=CondPermImp), fill=mycolours[8], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (% highly annoyed)\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

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" />

<!-- rnb-plot-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVYRzVwWmlBb2MyRjJaWEJzYjNSektYdGNiaUFnWjJkellYWmxLR1pwYkdWdVlXMWxQVndpVUhSelFVSmtTR2xCYm01dmVVUnBabVpXWVhKelEyOXVVR1Z5YldsdGNERndZeTV6ZG1kY0lpd2dkMmxrZEdnOU9Dd2dhR1ZwWjJoMFBUUXNJSEJoZEdnOVptbHNaUzV3WVhSb0tHOTFkRVpwWjFCaGRHZ3NJRndpYzNablhDSXBLVnh1SUNCMWJteHBibXNvWENKUWRITkJRbVJJYVVGdWJtOTVSR2xtWmxaaGNuTkRiMjVRWlhKdGFXMXdNWEJqTG5OMloxd2lLVnh1SUNCY2JpQWdaMmR6WVhabEtHWnBiR1Z1WVcxbFBWd2lVSFJ6UVVKa1NHbEJibTV2ZVVScFptWldZWEp6UTI5dVVHVnliV2x0Y0RGd1l5NXdaR1pjSWl3Z2QybGtkR2c5T0N3Z2FHVnBaMmgwUFRRc0lIQmhkR2c5Wm1sc1pTNXdZWFJvS0c5MWRFWnBaMUJoZEdnc0lGd2ljR1JtWENJcEtWeHVJQ0IxYm14cGJtc29YQ0pRZEhOQlFtUklhVUZ1Ym05NVJHbG1abFpoY25ORGIyNVFaWEp0YVcxd01YQmpMbkJrWmx3aUtWeHVmVnh1WEc1Z1lHQWlmUT09IC0tPlxuXG5gYGByXG5cbmlmIChzYXZlcGxvdHMpe1xuICBnZ3NhdmUoZmlsZW5hbWU9XFxQdHNBQmRIaUFubm95RGlmZlZhcnNDb25QZXJtaW1wMXBjLnN2Z1xcLCB3aWR0aD04LCBoZWlnaHQ9NCwgcGF0aD1maWxlLnBhdGgob3V0RmlnUGF0aCwgXFxzdmdcXCkpXG4gIHVubGluayhcXFB0c0FCZEhpQW5ub3lEaWZmVmFyc0NvblBlcm1pbXAxcGMuc3ZnXFwpXG4gIFxuICBnZ3NhdmUoZmlsZW5hbWU9XFxQdHNBQmRIaUFubm95RGlmZlZhcnNDb25QZXJtaW1wMXBjLnBkZlxcLCB3aWR0aD04LCBoZWlnaHQ9NCwgcGF0aD1maWxlLnBhdGgob3V0RmlnUGF0aCwgXFxwZGZcXCkpXG4gIHVubGluayhcXFB0c0FCZEhpQW5ub3lEaWZmVmFyc0NvblBlcm1pbXAxcGMucGRmXFwpXG59XG5cbmBgYFxuXG48IS0tIHJuYi1zb3VyY2UtZW5kIC0tPlxuIn0= -->

if (saveplots){
  ggsave(filename=\PtsABdHiAnnoyDiffVarsConPermimp1pc.svg\, width=8, height=4, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdHiAnnoyDiffVarsConPermimp1pc.svg\)
  
  ggsave(filename=\PtsABdHiAnnoyDiffVarsConPermimp1pc.pdf\, width=8, height=4, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdHiAnnoyDiffVarsConPermimp1pc.pdf\)
}



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


<!-- rnb-source-begin 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 -->

```r
```r

if (saveplots){
  ggsave(filename=\PtsABdHiAnnoyDiffVarsConPermimp1pc.svg\, width=8, height=4, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdHiAnnoyDiffVarsConPermimp1pc.svg\)
  
  ggsave(filename=\PtsABdHiAnnoyDiffVarsConPermimp1pc.pdf\, width=8, height=4, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdHiAnnoyDiffVarsConPermimp1pc.pdf\)
}

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Selected metric


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVYRzVrYVdabVZtRnlJRHd0SUZ3aVRFRkZURUZsY1dScFptWmNJbHh1WEc1Z1lHQWlmUT09IC0tPlxuXG5gYGByXG5cbmRpZmZWYXIgPC0gXFxMQUVMQWVxZGlmZlxcXG5cbmBgYFxuXG48IS0tIHJuYi1zb3VyY2UtZW5kIC0tPlxuIn0= -->

diffVar <- \LAELAeqdiff\



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dVhHNWthV1ptVm1GeUlEd3RJRnhjVEVGRlRFRmxjV1JwWm1aY1hGeHVYRzVnWUdCY2JtQmdZQ0o5IC0tPlxuXG5gYGByXG5gYGByXG5cbmRpZmZWYXIgPC0gXFxMQUVMQWVxZGlmZlxcXG5cbmBgYFxuYGBgXG5cbjwhLS0gcm5iLXNvdXJjZS1lbmQgLS0+XG4ifQ== -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5kaWZmVmFyIDwtIFxcTEFFTEFlcWRpZmZcXFxuXG5gYGBcbmBgYCJ9 -->

```r
```r

diffVar <- \LAELAeqdiff\

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


### dSQM analysis

The ambient-only stimuli are NOT replaced here, as the difference analysis includes dB metrics.

#### Individual SQMs

##### dSharpness

###### Set variables


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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iVars <- c(diffVar, eventVar, \dSharpAurISO3PowAvgMaxLR\, \dSharpAurISO305ExMaxLR\, \dSharpAurSHMPowAvgMaxLR\, \dSharpAurSHM05ExMaxLR\)
dVar <- \dHighAnnoyPc\

seeds <- c(84194, 905, 64815, 928054, 625091, 582031)



<!-- rnb-output-end -->

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```r
```r

iVars <- c(diffVar, eventVar, \dSharpAurISO3PowAvgMaxLR\, \dSharpAurISO305ExMaxLR\, \dSharpAurSHMPowAvgMaxLR\, \dSharpAurSHM05ExMaxLR\)
dVar <- \dHighAnnoyPc\

seeds <- c(84194, 905, 64815, 928054, 625091, 582031)

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


###### Hyperparameter tuning


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p



<!-- rnb-output-end -->

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```r
```r

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Selected hyperparameters


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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ntree <- 251
mtry <- as.integer(length(iVars)/1.75)



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dVhHNXVkSEpsWlNBOExTQXlOVEZjYm0xMGNua2dQQzBnWVhNdWFXNTBaV2RsY2loc1pXNW5kR2dvYVZaaGNuTXBMekV1TnpVcFhHNWNibUJnWUZ4dVlHQmdJbjA9IC0tPlxuXG5gYGByXG5gYGByXG5cbm50cmVlIDwtIDI1MVxubXRyeSA8LSBhcy5pbnRlZ2VyKGxlbmd0aChpVmFycykvMS43NSlcblxuYGBgXG5gYGBcblxuPCEtLSBybmItc291cmNlLWVuZCAtLT5cbiJ9 -->


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```r
```r

ntree <- 251
mtry <- as.integer(length(iVars)/1.75)

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->



###### Run model

Train preliminary model


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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nperm <- 5

resultsOutSharp <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSharp$OOB_RMSE
resultsOutSharp$OOB_MAE
resultsOutSharp$Rsquared



<!-- rnb-output-end -->

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```r
```r

nperm <- 5

resultsOutSharp <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSharp$OOB_RMSE
resultsOutSharp$OOB_MAE
resultsOutSharp$Rsquared

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->

Train multiple seeds model


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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resultsOutSharp <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSharp$OOB_RMSE
resultsOutSharp$OOB_MAE
resultsOutSharp$Rsquared



<!-- rnb-output-end -->

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```r
```r

resultsOutSharp <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSharp$OOB_RMSE
resultsOutSharp$OOB_MAE
resultsOutSharp$Rsquared

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->




<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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# store results
resdHiAnnoyFitAB['Diff sharp', 'RMSE'] <- resultsOutSharp$OOB_RMSE
resdHiAnnoyFitAB['Diff sharp', 'MAE'] <- resultsOutSharp$OOB_MAE
resdHiAnnoyFitAB['Diff sharp', 'Rsquared'] <- resultsOutSharp$Rsquared
resdHiAnnoyPermImpAB$DiffSharp <- resultsOutSharp$conditional_permimp



<!-- rnb-output-end -->

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```r
```r
# store results
resdHiAnnoyFitAB['Diff sharp', 'RMSE'] <- resultsOutSharp$OOB_RMSE
resdHiAnnoyFitAB['Diff sharp', 'MAE'] <- resultsOutSharp$OOB_MAE
resdHiAnnoyFitAB['Diff sharp', 'Rsquared'] <- resultsOutSharp$Rsquared
resdHiAnnoyPermImpAB$DiffSharp <- resultsOutSharp$conditional_permimp

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


###### Plot results


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutSharp.conimp <- arrange(resultsOutSharp$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutSharp.conimp) + geom_col(aes(x=factor(rownames(resultsOutSharp.conimp), levels=rownames(resultsOutSharp.conimp)), y=CondPermImp), fill=mycolours[2], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (% highly annoyed)\) + ggtitle(\dSharpness\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename=\PtsABdHiAnnoydSharpConPermimp.svg\, width=8, height=2.6, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdHiAnnoydSharpConPermimp.svg\)
  
  ggsave(filename=\PtsABdHiAnnoydSharpConPermimp.pdf\, width=8, height=2.6, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdHiAnnoydSharpConPermimp.pdf\)
}



<!-- rnb-output-end -->

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```r
```r
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutSharp.conimp <- arrange(resultsOutSharp$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutSharp.conimp) + geom_col(aes(x=factor(rownames(resultsOutSharp.conimp), levels=rownames(resultsOutSharp.conimp)), y=CondPermImp), fill=mycolours[2], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (% highly annoyed)\) + ggtitle(\dSharpness\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename=\PtsABdHiAnnoydSharpConPermimp.svg\, width=8, height=2.6, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdHiAnnoydSharpConPermimp.svg\)
  
  ggsave(filename=\PtsABdHiAnnoydSharpConPermimp.pdf\, width=8, height=2.6, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdHiAnnoydSharpConPermimp.pdf\)
}

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Selected metric


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVYRzVrVTJoaGNuQldZWElnUEMwZ1hDSmtVMmhoY25CQmRYSlRTRTFRYjNkQmRtZE5ZWGhNVWx3aVhHNWNibUJnWUNKOSAtLT5cblxuYGBgclxuXG5kU2hhcnBWYXIgPC0gXFxkU2hhcnBBdXJTSE1Qb3dBdmdNYXhMUlxcXG5cbmBgYFxuXG48IS0tIHJuYi1zb3VyY2UtZW5kIC0tPlxuIn0= -->

dSharpVar <- \dSharpAurSHMPowAvgMaxLR\



<!-- rnb-output-end -->

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<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5kU2hhcnBWYXIgPC0gXFxkU2hhcnBBdXJTSE1Qb3dBdmdNYXhMUlxcXG5cbmBgYFxuYGBgIn0= -->

```r
```r

dSharpVar <- \dSharpAurSHMPowAvgMaxLR\

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->



##### dTonal loudness and dtonality

###### Set variables


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->

iVars <- c(diffVar, eventVar, \dTonalECMAAvgMaxLR\, \dTonalSHMInt05ExMaxLR\, \dTonalSHMIntAvgMaxLR\, \dTonalECMA05ExMaxLR\, \dTonLdECMAPowAvgBin\, \dTonLdECMA05ExBin\)
dVar <- \dHighAnnoyPc\



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5pVmFycyA8LSBjKGRpZmZWYXIsIGV2ZW50VmFyLCBcXGRUb25hbEVDTUFBdmdNYXhMUlxcLCBcXGRUb25hbFNITUludDA1RXhNYXhMUlxcLCBcXGRUb25hbFNITUludEF2Z01heExSXFwsIFxcZFRvbmFsRUNNQTA1RXhNYXhMUlxcLCBcXGRUb25MZEVDTUFQb3dBdmdCaW5cXCwgXFxkVG9uTGRFQ01BMDVFeEJpblxcKVxuZFZhciA8LSBcXGRIaWdoQW5ub3lQY1xcXG5cbmBgYFxuYGBgIn0= -->

```r
```r

iVars <- c(diffVar, eventVar, \dTonalECMAAvgMaxLR\, \dTonalSHMInt05ExMaxLR\, \dTonalSHMIntAvgMaxLR\, \dTonalECMA05ExMaxLR\, \dTonLdECMAPowAvgBin\, \dTonLdECMA05ExBin\)
dVar <- \dHighAnnoyPc\

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


###### Hyperparameter tuning


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

seeds <- c(561684, 104798, 1536, 48, 48561)



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


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```r
```r

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

seeds <- c(561684, 104798, 1536, 48, 48561)

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

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<!-- rnb-text-begin -->

Selected hyperparameters


<!-- rnb-text-end -->


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<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVYRzV1ZEhKbFpTQThMU0F4TURBeFhHNXRkSEo1SUR3dElHRnpMbWx1ZEdWblpYSW9iR1Z1WjNSb0tHbFdZWEp6S1M4eExqSTFLVnh1WEc1Z1lHQWlmUT09IC0tPlxuXG5gYGByXG5cbm50cmVlIDwtIDEwMDFcbm10cnkgPC0gYXMuaW50ZWdlcihsZW5ndGgoaVZhcnMpLzEuMjUpXG5cbmBgYFxuXG48IS0tIHJuYi1zb3VyY2UtZW5kIC0tPlxuIn0= -->

ntree <- 1001
mtry <- as.integer(length(iVars)/1.25)



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dVhHNXVkSEpsWlNBOExTQXhNREF4WEc1dGRISjVJRHd0SUdGekxtbHVkR1ZuWlhJb2JHVnVaM1JvS0dsV1lYSnpLUzh4TGpJMUtWeHVYRzVnWUdCY2JtQmdZQ0o5IC0tPlxuXG5gYGByXG5gYGByXG5cbm50cmVlIDwtIDEwMDFcbm10cnkgPC0gYXMuaW50ZWdlcihsZW5ndGgoaVZhcnMpLzEuMjUpXG5cbmBgYFxuYGBgXG5cbjwhLS0gcm5iLXNvdXJjZS1lbmQgLS0+XG4ifQ== -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5udHJlZSA8LSAxMDAxXG5tdHJ5IDwtIGFzLmludGVnZXIobGVuZ3RoKGlWYXJzKS8xLjI1KVxuXG5gYGBcbmBgYCJ9 -->

```r
```r

ntree <- 1001
mtry <- as.integer(length(iVars)/1.25)

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

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<!-- rnb-text-begin -->


###### Run model

Train preliminary model


<!-- rnb-text-end -->


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<!-- rnb-output-begin 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 -->
# Tonality with tonal loudness

nperm <- 5

resultsOutTonal1 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutTonal1$OOB_RMSE
resultsOutTonal1$OOB_MAE
resultsOutTonal1$Rsquared



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


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```r
```r
# Tonality with tonal loudness

nperm <- 5

resultsOutTonal1 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutTonal1$OOB_RMSE
resultsOutTonal1$OOB_MAE
resultsOutTonal1$Rsquared
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

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<!-- rnb-text-begin -->


Train multiple seeds model


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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# Tonality with tonal loudness

resultsOutTonal1 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutTonal1$OOB_RMSE
resultsOutTonal1$OOB_MAE
resultsOutTonal1$Rsquared



<!-- rnb-output-end -->

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```r
```r
# Tonality with tonal loudness

resultsOutTonal1 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutTonal1$OOB_RMSE
resultsOutTonal1$OOB_MAE
resultsOutTonal1$Rsquared

```
```

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# store results
resdHiAnnoyFitAB['Diff tonal inc loud', 'RMSE'] <- resultsOutTonal1$OOB_RMSE
resdHiAnnoyFitAB['Diff tonal inc loud', 'MAE'] <- resultsOutTonal1$OOB_MAE
resdHiAnnoyFitAB['Diff tonal inc loud', 'Rsquared'] <- resultsOutTonal1$Rsquared
resdHiAnnoyPermImpAB$DiffTonal1 <- resultsOutTonal1$conditional_permimp



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```r
```r
# store results
resdHiAnnoyFitAB['Diff tonal inc loud', 'RMSE'] <- resultsOutTonal1$OOB_RMSE
resdHiAnnoyFitAB['Diff tonal inc loud', 'MAE'] <- resultsOutTonal1$OOB_MAE
resdHiAnnoyFitAB['Diff tonal inc loud', 'Rsquared'] <- resultsOutTonal1$Rsquared
resdHiAnnoyPermImpAB$DiffTonal1 <- resultsOutTonal1$conditional_permimp

```
```

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###### Plot results


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par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutTonal1.conimp <- arrange(resultsOutTonal1$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutTonal1.conimp) + geom_col(aes(x=factor(rownames(resultsOutTonal1.conimp), levels=rownames(resultsOutTonal1.conimp)), y=CondPermImp), fill=mycolours[3], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (% highly annoyed)\) + ggtitle(\dTonality inc. dtonal loudness\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 50))
pBar

if (saveplots){
  ggsave(filename=\PtsABdHiAnnoydTonalLdConPermimp.svg\, width=8, height=2.6, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdHiAnnoydTonalLdConPermimp.svg\)
  
  ggsave(filename=\PtsABdHiAnnoydTonalLdConPermimp.pdf\, width=8, height=2.6, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdHiAnnoydTonalLdConPermimp.pdf\)
}



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```r
```r

par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutTonal1.conimp <- arrange(resultsOutTonal1$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutTonal1.conimp) + geom_col(aes(x=factor(rownames(resultsOutTonal1.conimp), levels=rownames(resultsOutTonal1.conimp)), y=CondPermImp), fill=mycolours[3], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (% highly annoyed)\) + ggtitle(\dTonality inc. dtonal loudness\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 50))
pBar

if (saveplots){
  ggsave(filename=\PtsABdHiAnnoydTonalLdConPermimp.svg\, width=8, height=2.6, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdHiAnnoydTonalLdConPermimp.svg\)
  
  ggsave(filename=\PtsABdHiAnnoydTonalLdConPermimp.pdf\, width=8, height=2.6, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdHiAnnoydTonalLdConPermimp.pdf\)
}

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Selected metric


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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dTonLdVar <- \dTonLdECMAPowAvgBin\



<!-- rnb-output-end -->

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```r
```r

dTonLdVar <- \dTonLdECMAPowAvgBin\

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


##### dTonality without dtonal loudness

###### Set variables


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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iVars <- c(diffVar, eventVar, \dTonalECMAAvgMaxLR\, \dTonalSHMInt05ExMaxLR\, \dTonalSHMIntAvgMaxLR\, \dTonalECMA05ExMaxLR\)
dVar <- \dHighAnnoyPc\

seeds <- c(410865, 2954, 70812, 203, 7984)



<!-- rnb-output-end -->

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```r
```r

iVars <- c(diffVar, eventVar, \dTonalECMAAvgMaxLR\, \dTonalSHMInt05ExMaxLR\, \dTonalSHMIntAvgMaxLR\, \dTonalECMA05ExMaxLR\)
dVar <- \dHighAnnoyPc\

seeds <- c(410865, 2954, 70812, 203, 7984)

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


###### Hyperparameter tuning


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p



<!-- rnb-output-end -->

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```r
```r

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Selected hyperparameters


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVYRzV1ZEhKbFpTQThMU0EwTURBeFhHNXRkSEo1SUR3dElHRnpMbWx1ZEdWblpYSW9iR1Z1WjNSb0tHbFdZWEp6S1M4eExqYzFLVnh1WEc1Z1lHQWlmUT09IC0tPlxuXG5gYGByXG5cbm50cmVlIDwtIDQwMDFcbm10cnkgPC0gYXMuaW50ZWdlcihsZW5ndGgoaVZhcnMpLzEuNzUpXG5cbmBgYFxuXG48IS0tIHJuYi1zb3VyY2UtZW5kIC0tPlxuIn0= -->

ntree <- 4001
mtry <- as.integer(length(iVars)/1.75)



<!-- rnb-output-end -->

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```r
```r

ntree <- 4001
mtry <- as.integer(length(iVars)/1.75)

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->



###### Run model

Train preliminary model


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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# Tonality

nperm <- 5

resultsOutTonal2 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutTonal2$OOB_RMSE
resultsOutTonal2$OOB_MAE
resultsOutTonal2$Rsquared



<!-- rnb-output-end -->

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```r
```r
# Tonality

nperm <- 5

resultsOutTonal2 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutTonal2$OOB_RMSE
resultsOutTonal2$OOB_MAE
resultsOutTonal2$Rsquared

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Train multiple seeds model


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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# Tonality

resultsOutTonal2 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutTonal2$OOB_RMSE
resultsOutTonal2$OOB_MAE
resultsOutTonal2$Rsquared



<!-- rnb-output-end -->

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```r
```r
# Tonality

resultsOutTonal2 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutTonal2$OOB_RMSE
resultsOutTonal2$OOB_MAE
resultsOutTonal2$Rsquared

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->




<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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# store results
resdHiAnnoyFitAB['Diff tonal no loud', 'RMSE'] <- resultsOutTonal2$OOB_RMSE
resdHiAnnoyFitAB['Diff tonal no loud', 'MAE'] <- resultsOutTonal2$OOB_MAE
resdHiAnnoyFitAB['Diff tonal no loud', 'Rsquared'] <- resultsOutTonal2$Rsquared
resdHiAnnoyPermImpAB$DiffTonal2 <- resultsOutTonal2$conditional_permimp



<!-- rnb-output-end -->

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```r
```r

# store results
resdHiAnnoyFitAB['Diff tonal no loud', 'RMSE'] <- resultsOutTonal2$OOB_RMSE
resdHiAnnoyFitAB['Diff tonal no loud', 'MAE'] <- resultsOutTonal2$OOB_MAE
resdHiAnnoyFitAB['Diff tonal no loud', 'Rsquared'] <- resultsOutTonal2$Rsquared
resdHiAnnoyPermImpAB$DiffTonal2 <- resultsOutTonal2$conditional_permimp

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


###### Plot results


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutTonal2.conimp <- arrange(resultsOutTonal2$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutTonal2.conimp) + geom_col(aes(x=factor(rownames(resultsOutTonal2.conimp), levels=rownames(resultsOutTonal2.conimp)), y=CondPermImp), fill=mycolours[3], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (% highly annoyed)\) + ggtitle(\dTonality w/o tonal loudness\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 50))
pBar

if (saveplots){
  ggsave(filename=\PtsABdHiAnnoydTonalConPermimp.svg\, width=8, height=2, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdHiAnnoydTonalConPermimp.svg\)
  
  ggsave(filename=\PtsABdHiAnnoydTonalConPermimp.pdf\, width=8, height=2, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdHiAnnoydTonalConPermimp.pdf\)
}



<!-- rnb-output-end -->

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```r
```r
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutTonal2.conimp <- arrange(resultsOutTonal2$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutTonal2.conimp) + geom_col(aes(x=factor(rownames(resultsOutTonal2.conimp), levels=rownames(resultsOutTonal2.conimp)), y=CondPermImp), fill=mycolours[3], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (% highly annoyed)\) + ggtitle(\dTonality w/o tonal loudness\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 50))
pBar

if (saveplots){
  ggsave(filename=\PtsABdHiAnnoydTonalConPermimp.svg\, width=8, height=2, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdHiAnnoydTonalConPermimp.svg\)
  
  ggsave(filename=\PtsABdHiAnnoydTonalConPermimp.pdf\, width=8, height=2, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdHiAnnoydTonalConPermimp.pdf\)
}

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Selected metric


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVYRzVrVkc5dVlXeFdZWElnUEMwZ1hDSmtWRzl1WVd4VFNFMUpiblJCZG1kTllYaE1VbHdpWEc1Y2JtQmdZQ0o5IC0tPlxuXG5gYGByXG5cbmRUb25hbFZhciA8LSBcXGRUb25hbFNITUludEF2Z01heExSXFxcblxuYGBgXG5cbjwhLS0gcm5iLXNvdXJjZS1lbmQgLS0+XG4ifQ== -->

dTonalVar <- \dTonalSHMIntAvgMaxLR\



<!-- rnb-output-end -->

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```r
```r

dTonalVar <- \dTonalSHMIntAvgMaxLR\

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

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<!-- rnb-text-begin -->


##### dFluctuation strength

###### Set variables


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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# Fluctuation strength
iVars <- c(diffVar, eventVar, \dFluctSHM10ExBin\, \dFluctSHM05ExBin\, \dFluctOV10ExMaxLR\, \dFluctOV05ExMaxLR\)
dVar <- \dHighAnnoyPc\

seeds <- c(418657, 84, 1630, 18659, 3687)



<!-- rnb-output-end -->

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```r
```r

# Fluctuation strength
iVars <- c(diffVar, eventVar, \dFluctSHM10ExBin\, \dFluctSHM05ExBin\, \dFluctOV10ExMaxLR\, \dFluctOV05ExMaxLR\)
dVar <- \dHighAnnoyPc\

seeds <- c(418657, 84, 1630, 18659, 3687)

```
```

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###### Hyperparameter tuning


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p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p



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```r
```r

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```
```

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Selected hyperparameters


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ntree <- 251
mtry <- as.integer(length(iVars)/1.75)



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```r
```r

ntree <- 251
mtry <- as.integer(length(iVars)/1.75)

```
```

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###### Run model

Train preliminary model


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nperm <- 5

resultsOutFluct <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutFluct$OOB_RMSE
resultsOutFluct$OOB_MAE
resultsOutFluct$Rsquared



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```r
```r

nperm <- 5

resultsOutFluct <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutFluct$OOB_RMSE
resultsOutFluct$OOB_MAE
resultsOutFluct$Rsquared

```
```

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Train multiple seeds model


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resultsOutFluct <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutFluct$OOB_RMSE
resultsOutFluct$OOB_MAE
resultsOutFluct$Rsquared



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```r
```r

resultsOutFluct <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutFluct$OOB_RMSE
resultsOutFluct$OOB_MAE
resultsOutFluct$Rsquared

```
```

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# store results
resdHiAnnoyFitAB['Diff fluct', 'RMSE'] <- resultsOutFluct$OOB_RMSE
resdHiAnnoyFitAB['Diff fluct', 'MAE'] <- resultsOutFluct$OOB_MAE
resdHiAnnoyFitAB['Diff fluct', 'Rsquared'] <- resultsOutFluct$Rsquared
resdHiAnnoyPermImpAB$DiffFluct <- resultsOutFluct$conditional_permimp



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```r
```r

# store results
resdHiAnnoyFitAB['Diff fluct', 'RMSE'] <- resultsOutFluct$OOB_RMSE
resdHiAnnoyFitAB['Diff fluct', 'MAE'] <- resultsOutFluct$OOB_MAE
resdHiAnnoyFitAB['Diff fluct', 'Rsquared'] <- resultsOutFluct$Rsquared
resdHiAnnoyPermImpAB$DiffFluct <- resultsOutFluct$conditional_permimp

```
```

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###### Plot results


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par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutFluct.conimp <- arrange(resultsOutFluct$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutFluct.conimp) + geom_col(aes(x=factor(rownames(resultsOutFluct.conimp), levels=rownames(resultsOutFluct.conimp)), y=CondPermImp), fill=mycolours[4], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (% highly annoyed)\) + ggtitle(\dFluctuation strength\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename=\PtsABdHiAnnoydFluctConPermimp.svg\, width=8, height=2, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdHiAnnoyFluctConPermimp.svg\)
  
  ggsave(filename=\PtsABdHiAnnoydFluctConPermimp.pdf\, width=8, height=2, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdHiAnnoyFluctConPermimp.pdf\)
}



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```r
```r
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutFluct.conimp <- arrange(resultsOutFluct$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutFluct.conimp) + geom_col(aes(x=factor(rownames(resultsOutFluct.conimp), levels=rownames(resultsOutFluct.conimp)), y=CondPermImp), fill=mycolours[4], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (% highly annoyed)\) + ggtitle(\dFluctuation strength\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename=\PtsABdHiAnnoydFluctConPermimp.svg\, width=8, height=2, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdHiAnnoyFluctConPermimp.svg\)
  
  ggsave(filename=\PtsABdHiAnnoydFluctConPermimp.pdf\, width=8, height=2, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdHiAnnoyFluctConPermimp.pdf\)
}

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Selected metric


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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dFluctVar <- \dFluctSHM10ExBin\



<!-- rnb-output-end -->

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```r
```r

dFluctVar <- \dFluctSHM10ExBin\

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


##### dRoughness

###### Set variables


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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# Roughness
iVars <- c(diffVar, eventVar, \dRoughECMA10ExBin\, \dRoughECMA05ExBin\, \dRoughFZ10ExMaxLR\, \dRoughFZ05ExMaxLR\)
dVar <- \dHighAnnoyPc\

seeds <- c(69851, 85109, 410986, 1563, 896)



<!-- rnb-output-end -->

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```r
```r

# Roughness
iVars <- c(diffVar, eventVar, \dRoughECMA10ExBin\, \dRoughECMA05ExBin\, \dRoughFZ10ExMaxLR\, \dRoughFZ05ExMaxLR\)
dVar <- \dHighAnnoyPc\

seeds <- c(69851, 85109, 410986, 1563, 896)

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


###### Hyperparameter tuning


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p



<!-- rnb-output-end -->

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```r
```r

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Selected hyperparameters


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVYRzV1ZEhKbFpTQThMU0F5TlRGY2JtMTBjbmtnUEMwZ1lYTXVhVzUwWldkbGNpaHNaVzVuZEdnb2FWWmhjbk1wTHpFdU56VXBYRzVjYm1CZ1lDSjkgLS0+XG5cbmBgYHJcblxubnRyZWUgPC0gMjUxXG5tdHJ5IDwtIGFzLmludGVnZXIobGVuZ3RoKGlWYXJzKS8xLjc1KVxuXG5gYGBcblxuPCEtLSBybmItc291cmNlLWVuZCAtLT5cbiJ9 -->

ntree <- 251
mtry <- as.integer(length(iVars)/1.75)



<!-- rnb-output-end -->

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```r
```r

ntree <- 251
mtry <- as.integer(length(iVars)/1.75)

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


###### Run model

Train preliminary model


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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nperm <- 5

resultsOutRough <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutRough$OOB_RMSE
resultsOutRough$OOB_MAE
resultsOutRough$Rsquared



<!-- rnb-output-end -->

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```r
```r

nperm <- 5

resultsOutRough <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutRough$OOB_RMSE
resultsOutRough$OOB_MAE
resultsOutRough$Rsquared

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Train multiple seeds model


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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resultsOutRough <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutRough$OOB_RMSE
resultsOutRough$OOB_MAE
resultsOutRough$Rsquared



<!-- rnb-output-end -->

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```r
```r

resultsOutRough <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutRough$OOB_RMSE
resultsOutRough$OOB_MAE
resultsOutRough$Rsquared

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->



<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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# store results
resdHiAnnoyFitAB['Diff rough', 'RMSE'] <- resultsOutRough$OOB_RMSE
resdHiAnnoyFitAB['Diff rough', 'MAE'] <- resultsOutRough$OOB_MAE
resdHiAnnoyFitAB['Diff rough', 'Rsquared'] <- resultsOutRough$Rsquared
resdHiAnnoyPermImpAB$DiffRough <- resultsOutRough$conditional_permimp



<!-- rnb-output-end -->

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```r
```r
# store results
resdHiAnnoyFitAB['Diff rough', 'RMSE'] <- resultsOutRough$OOB_RMSE
resdHiAnnoyFitAB['Diff rough', 'MAE'] <- resultsOutRough$OOB_MAE
resdHiAnnoyFitAB['Diff rough', 'Rsquared'] <- resultsOutRough$Rsquared
resdHiAnnoyPermImpAB$DiffRough <- resultsOutRough$conditional_permimp

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


###### Plot results


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutRough.conimp <- arrange(resultsOutRough$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutRough.conimp) + geom_col(aes(x=factor(rownames(resultsOutRough.conimp), levels=rownames(resultsOutRough.conimp)), y=CondPermImp), fill=mycolours[5], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (% highly annoyed)\) + ggtitle(\dRoughness\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename=\PtsABdHiAnnoydRoughConPermimp.svg\, width=8, height=2, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdHiAnnoydRoughConPermimp.svg\)
  
  ggsave(filename=\PtsABdHiAnnoydRoughConPermimp.pdf\, width=8, height=2, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdHiAnnoydRoughConPermimp.pdf\)
}



<!-- rnb-output-end -->

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```r
```r
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutRough.conimp <- arrange(resultsOutRough$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutRough.conimp) + geom_col(aes(x=factor(rownames(resultsOutRough.conimp), levels=rownames(resultsOutRough.conimp)), y=CondPermImp), fill=mycolours[5], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (% highly annoyed)\) + ggtitle(\dRoughness\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename=\PtsABdHiAnnoydRoughConPermimp.svg\, width=8, height=2, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdHiAnnoydRoughConPermimp.svg\)
  
  ggsave(filename=\PtsABdHiAnnoydRoughConPermimp.pdf\, width=8, height=2, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdHiAnnoydRoughConPermimp.pdf\)
}

```
```

<!-- rnb-source-end -->


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<!-- rnb-text-begin -->


Selected metric


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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dRoughVar <- \dRoughFZ05ExMaxLR\



<!-- rnb-output-end -->

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```r
```r

dRoughVar <- \dRoughFZ05ExMaxLR\

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

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<!-- rnb-text-begin -->


##### dImpulsiveness

###### Set variables


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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# Impulsiveness
iVars <- c(diffVar, eventVar, \dImpulsSHMAvgMaxLR\, \dImpulsSHM05ExMaxLR\, \dImpulsSHMPowAvgMaxLR\)
dVar <- \dHighAnnoyPc\

seeds <- c(418659, 7805, 38475, 65834, 1653)



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuIyBJbXB1bHNpdmVuZXNzXG5pVmFycyA8LSBjKGRpZmZWYXIsIGV2ZW50VmFyLCBcXGRJbXB1bHNTSE1BdmdNYXhMUlxcLCBcXGRJbXB1bHNTSE0wNUV4TWF4TFJcXCwgXFxkSW1wdWxzU0hNUG93QXZnTWF4TFJcXClcbmRWYXIgPC0gXFxkSGlnaEFubm95UGNcXFxuXG5zZWVkcyA8LSBjKDQxODY1OSwgNzgwNSwgMzg0NzUsIDY1ODM0LCAxNjUzKVxuXG5gYGBcbmBgYCJ9 -->

```r
```r
# Impulsiveness
iVars <- c(diffVar, eventVar, \dImpulsSHMAvgMaxLR\, \dImpulsSHM05ExMaxLR\, \dImpulsSHMPowAvgMaxLR\)
dVar <- \dHighAnnoyPc\

seeds <- c(418659, 7805, 38475, 65834, 1653)

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


###### Hyperparameter tuning


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5wIDwtIG10cnlUdW5lKGRhdGFJbj1zdGltRGF0YU51bSwgaVZhcnM9aVZhcnMsIGRWYXI9ZFZhciwgc2VlZD1zZWVkc1sxOjJdLFxuICAgICAgICAgICAgIG50cmVlcz1udHJlZXMsIG1pbnNwbGl0PW1pbnNwbGl0LCBtaW5idWNrZXQ9bWluYnVja2V0KVxucFxuXG5gYGBcbmBgYCJ9 -->

```r
```r

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Selected hyperparameters


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVYRzV1ZEhKbFpTQThMU0F4TURBeFhHNXRkSEo1SUR3dElHRnpMbWx1ZEdWblpYSW9iR1Z1WjNSb0tHbFdZWEp6S1M4eExqSTFLVnh1WEc1Z1lHQWlmUT09IC0tPlxuXG5gYGByXG5cbm50cmVlIDwtIDEwMDFcbm10cnkgPC0gYXMuaW50ZWdlcihsZW5ndGgoaVZhcnMpLzEuMjUpXG5cbmBgYFxuXG48IS0tIHJuYi1zb3VyY2UtZW5kIC0tPlxuIn0= -->

ntree <- 1001
mtry <- as.integer(length(iVars)/1.25)



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dVhHNXVkSEpsWlNBOExTQXhNREF4WEc1dGRISjVJRHd0SUdGekxtbHVkR1ZuWlhJb2JHVnVaM1JvS0dsV1lYSnpLUzh4TGpJMUtWeHVYRzVnWUdCY2JtQmdZQ0o5IC0tPlxuXG5gYGByXG5gYGByXG5cbm50cmVlIDwtIDEwMDFcbm10cnkgPC0gYXMuaW50ZWdlcihsZW5ndGgoaVZhcnMpLzEuMjUpXG5cbmBgYFxuYGBgXG5cbjwhLS0gcm5iLXNvdXJjZS1lbmQgLS0+XG4ifQ== -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5udHJlZSA8LSAxMDAxXG5tdHJ5IDwtIGFzLmludGVnZXIobGVuZ3RoKGlWYXJzKS8xLjI1KVxuXG5gYGBcbmBgYCJ9 -->

```r
```r

ntree <- 1001
mtry <- as.integer(length(iVars)/1.25)

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->



###### Run model

Train preliminary model


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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nperm <- 5

resultsOutImpuls <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutImpuls$OOB_RMSE
resultsOutImpuls$OOB_MAE
resultsOutImpuls$Rsquared



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


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```r
```r

nperm <- 5

resultsOutImpuls <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutImpuls$OOB_RMSE
resultsOutImpuls$OOB_MAE
resultsOutImpuls$Rsquared

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Train multiple seeds model


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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resultsOutImpuls <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutImpuls$OOB_RMSE
resultsOutImpuls$OOB_MAE
resultsOutImpuls$Rsquared



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


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```r
```r

resultsOutImpuls <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutImpuls$OOB_RMSE
resultsOutImpuls$OOB_MAE
resultsOutImpuls$Rsquared

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->



<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->

# store results
resdHiAnnoyFitAB['Diff impuls', 'RMSE'] <- resultsOutImpuls$OOB_RMSE
resdHiAnnoyFitAB['Diff impuls', 'MAE'] <- resultsOutImpuls$OOB_MAE
resdHiAnnoyFitAB['Diff impuls', 'Rsquared'] <- resultsOutImpuls$Rsquared
resdHiAnnoyPermImpAB$DiffImpuls <- resultsOutImpuls$conditional_permimp



<!-- rnb-output-end -->

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```r
```r

# store results
resdHiAnnoyFitAB['Diff impuls', 'RMSE'] <- resultsOutImpuls$OOB_RMSE
resdHiAnnoyFitAB['Diff impuls', 'MAE'] <- resultsOutImpuls$OOB_MAE
resdHiAnnoyFitAB['Diff impuls', 'Rsquared'] <- resultsOutImpuls$Rsquared
resdHiAnnoyPermImpAB$DiffImpuls <- resultsOutImpuls$conditional_permimp

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

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<!-- rnb-text-begin -->


###### Plot results


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<!-- rnb-chunk-begin -->


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par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutImpuls.conimp <- arrange(resultsOutImpuls$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutImpuls.conimp) + geom_col(aes(x=factor(rownames(resultsOutImpuls.conimp), levels=rownames(resultsOutImpuls.conimp)), y=CondPermImp), fill=mycolours[6], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (% highly annoyed)\) + ggtitle(\dImpulsiveness\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename=\PtsABdHiAnnoydImpulsConPermimp.svg\, width=8, height=2, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdHiAnnoydImpulsConPermimp.svg\)
  
  ggsave(filename=\PtsABdHiAnnoydImpulsConPermimp.pdf\, width=8, height=2, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdHiAnnoydImpulsConPermimp.pdf\)
}



<!-- rnb-output-end -->

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```r
```r
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutImpuls.conimp <- arrange(resultsOutImpuls$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutImpuls.conimp) + geom_col(aes(x=factor(rownames(resultsOutImpuls.conimp), levels=rownames(resultsOutImpuls.conimp)), y=CondPermImp), fill=mycolours[6], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (% highly annoyed)\) + ggtitle(\dImpulsiveness\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename=\PtsABdHiAnnoydImpulsConPermimp.svg\, width=8, height=2, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdHiAnnoydImpulsConPermimp.svg\)
  
  ggsave(filename=\PtsABdHiAnnoydImpulsConPermimp.pdf\, width=8, height=2, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdHiAnnoydImpulsConPermimp.pdf\)
}

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

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<!-- rnb-text-begin -->


Selected metric


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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dImpulsVar <- \dImpulsSHMAvgMaxLR\



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```r
```r

dImpulsVar <- \dImpulsSHMAvgMaxLR\

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

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<!-- rnb-text-begin -->


#### dSQM and loudness comparison

Now the highest importance dSQMs are ranked against each other, controlling for loudness difference.

##### Include dtonal loudness

###### Set variables


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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iVars <- c(diffVar, eventVar, dSharpVar, dTonLdVar, dFluctVar, dRoughVar, dImpulsVar)
dVar <- \dHighAnnoyPc\

seeds <- c(98465, 54163, 6541, 36485, 849675)



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```r
```r

iVars <- c(diffVar, eventVar, dSharpVar, dTonLdVar, dFluctVar, dRoughVar, dImpulsVar)
dVar <- \dHighAnnoyPc\

seeds <- c(98465, 54163, 6541, 36485, 849675)

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


###### Hyperparameter tuning


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p



<!-- rnb-output-end -->

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```r
```r

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Selected hyperparameters


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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ntree <- 501
mtry <- as.integer(length(iVars)/1.25)



<!-- rnb-output-end -->

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```r
```r

ntree <- 501
mtry <- as.integer(length(iVars)/1.25)

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

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<!-- rnb-text-begin -->



###### Run model

Train preliminary model


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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nperm <- 5

resultsOutSQMs1 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSQMs1$OOB_RMSE
resultsOutSQMs1$OOB_MAE
resultsOutSQMs1$Rsquared



<!-- rnb-output-end -->

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```r
```r

nperm <- 5

resultsOutSQMs1 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSQMs1$OOB_RMSE
resultsOutSQMs1$OOB_MAE
resultsOutSQMs1$Rsquared

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Train multiple seeds model


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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resultsOutSQMs1 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSQMs1$OOB_RMSE
resultsOutSQMs1$OOB_MAE
resultsOutSQMs1$Rsquared



<!-- rnb-output-end -->

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```r
```r

resultsOutSQMs1 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSQMs1$OOB_RMSE
resultsOutSQMs1$OOB_MAE
resultsOutSQMs1$Rsquared

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->



<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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# store results
resdHiAnnoyFitAB['Diff SQMs inc tonal loud', 'RMSE'] <- resultsOutSQMs1$OOB_RMSE
resdHiAnnoyFitAB['Diff SQMs inc tonal loud', 'MAE'] <- resultsOutSQMs1$OOB_MAE
resdHiAnnoyFitAB['Diff SQMs inc tonal loud', 'Rsquared'] <- resultsOutSQMs1$Rsquared
resdHiAnnoyPermImpAB$DiffSQMs1 <- resultsOutSQMs1$conditional_permimp



<!-- rnb-output-end -->

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```r
```r

# store results
resdHiAnnoyFitAB['Diff SQMs inc tonal loud', 'RMSE'] <- resultsOutSQMs1$OOB_RMSE
resdHiAnnoyFitAB['Diff SQMs inc tonal loud', 'MAE'] <- resultsOutSQMs1$OOB_MAE
resdHiAnnoyFitAB['Diff SQMs inc tonal loud', 'Rsquared'] <- resultsOutSQMs1$Rsquared
resdHiAnnoyPermImpAB$DiffSQMs1 <- resultsOutSQMs1$conditional_permimp

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


###### Plot results


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutSQMs1.conimp <- arrange(resultsOutSQMs1$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutSQMs1.conimp) + geom_col(aes(x=factor(rownames(resultsOutSQMs1.conimp), levels=rownames(resultsOutSQMs1.conimp)), y=CondPermImp), fill=mycolours[7], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (% highly annoyed)\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 50))
pBar

if (saveplots){
  ggsave(filename=\PtsABdHiAnnoyDiffSQMsTonLdConPermimp.svg\, width=8, height=2.4, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdHiAnnoyDiffSQMsTonLdConPermimp.svg\)
  
  ggsave(filename=\PtsABdHiAnnoyDiffSQMsTonLdConPermimp.pdf\, width=8, height=2.4, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdHiAnnoyDiffSQMsTonLdConPermimp.pdf\)
}



<!-- rnb-output-end -->

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```r
```r
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutSQMs1.conimp <- arrange(resultsOutSQMs1$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutSQMs1.conimp) + geom_col(aes(x=factor(rownames(resultsOutSQMs1.conimp), levels=rownames(resultsOutSQMs1.conimp)), y=CondPermImp), fill=mycolours[7], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (% highly annoyed)\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 50))
pBar

if (saveplots){
  ggsave(filename=\PtsABdHiAnnoyDiffSQMsTonLdConPermimp.svg\, width=8, height=2.4, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdHiAnnoyDiffSQMsTonLdConPermimp.svg\)
  
  ggsave(filename=\PtsABdHiAnnoyDiffSQMsTonLdConPermimp.pdf\, width=8, height=2.4, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdHiAnnoyDiffSQMsTonLdConPermimp.pdf\)
}

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


##### Exclude tonal loudness

###### Set variables


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->

iVars <- c(diffVar, eventVar, dSharpVar, dTonalVar, dFluctVar, dRoughVar, dImpulsVar)
dVar <- \dHighAnnoyPc\

seeds <- c(49865, 7852, 845961, 410583, 36748)



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


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```r
```r

iVars <- c(diffVar, eventVar, dSharpVar, dTonalVar, dFluctVar, dRoughVar, dImpulsVar)
dVar <- \dHighAnnoyPc\

seeds <- c(49865, 7852, 845961, 410583, 36748)

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

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<!-- rnb-text-begin -->


###### Hyperparameter tuning


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p



<!-- rnb-output-end -->

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<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5wIDwtIG10cnlUdW5lKGRhdGFJbj1zdGltRGF0YU51bSwgaVZhcnM9aVZhcnMsIGRWYXI9ZFZhciwgc2VlZD1zZWVkc1sxOjJdLFxuICAgICAgICAgICAgIG50cmVlcz1udHJlZXMsIG1pbnNwbGl0PW1pbnNwbGl0LCBtaW5idWNrZXQ9bWluYnVja2V0KVxucFxuXG5gYGBcbmBgYCJ9 -->

```r
```r

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Selected hyperparameters


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVYRzV1ZEhKbFpTQThMU0F5TlRGY2JtMTBjbmtnUEMwZ1lYTXVhVzUwWldkbGNpaHNaVzVuZEdnb2FWWmhjbk1wTHpFdU1qVXBYRzVjYm1CZ1lDSjkgLS0+XG5cbmBgYHJcblxubnRyZWUgPC0gMjUxXG5tdHJ5IDwtIGFzLmludGVnZXIobGVuZ3RoKGlWYXJzKS8xLjI1KVxuXG5gYGBcblxuPCEtLSBybmItc291cmNlLWVuZCAtLT5cbiJ9 -->

ntree <- 251
mtry <- as.integer(length(iVars)/1.25)



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dVhHNXVkSEpsWlNBOExTQXlOVEZjYm0xMGNua2dQQzBnWVhNdWFXNTBaV2RsY2loc1pXNW5kR2dvYVZaaGNuTXBMekV1TWpVcFhHNWNibUJnWUZ4dVlHQmdJbjA9IC0tPlxuXG5gYGByXG5gYGByXG5cbm50cmVlIDwtIDI1MVxubXRyeSA8LSBhcy5pbnRlZ2VyKGxlbmd0aChpVmFycykvMS4yNSlcblxuYGBgXG5gYGBcblxuPCEtLSBybmItc291cmNlLWVuZCAtLT5cbiJ9 -->


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```r
```r

ntree <- 251
mtry <- as.integer(length(iVars)/1.25)

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


###### Run model

Train preliminary model


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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nperm <- 5

resultsOutSQMs2 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSQMs2$OOB_RMSE
resultsOutSQMs2$OOB_MAE
resultsOutSQMs2$Rsquared



<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dVhHNXVjR1Z5YlNBOExTQTFYRzVjYm5KbGMzVnNkSE5QZFhSVFVVMXpNaUE4TFNCamNtWlNaV2NvWkdGMFlVbHVQWE4wYVcxRVlYUmhUblZ0TENCcFZtRnljejFwVm1GeWN5d2daRlpoY2oxa1ZtRnlMQ0J6WldWa2N6MXpaV1ZrYzFzeE9qSmRMQ0J1ZEhKbFpUMXVkSEpsWlN3Z2JYUnllVDF0ZEhKNUxDQndaWEp0U1cxd1EyOXVaRlJvY21WelBYQmxjbTFKYlhCRGIyNWtWR2h5WlhNc0lHNXdaWEp0UFc1d1pYSnRMQ0J0YVc1emNHeHBkRDF0YVc1emNHeHBkQ3dnYldsdVluVmphMlYwUFcxcGJtSjFZMnRsZENsY2JseHVJeUJ3Y21sdWRDQnRiMlJsYkNCd2NtVmthV04wYVc5dUlISmxjM1ZzZEhOY2JuSmxjM1ZzZEhOUGRYUlRVVTF6TWlSUFQwSmZVazFUUlZ4dWNtVnpkV3gwYzA5MWRGTlJUWE15SkU5UFFsOU5RVVZjYm5KbGMzVnNkSE5QZFhSVFVVMXpNaVJTYzNGMVlYSmxaRnh1WEc1Z1lHQmNibUJnWUNKOSAtLT5cblxuYGBgclxuYGBgclxuXG5ucGVybSA8LSA1XG5cbnJlc3VsdHNPdXRTUU1zMiA8LSBjcmZSZWcoZGF0YUluPXN0aW1EYXRhTnVtLCBpVmFycz1pVmFycywgZFZhcj1kVmFyLCBzZWVkcz1zZWVkc1sxOjJdLCBudHJlZT1udHJlZSwgbXRyeT1tdHJ5LCBwZXJtSW1wQ29uZFRocmVzPXBlcm1JbXBDb25kVGhyZXMsIG5wZXJtPW5wZXJtLCBtaW5zcGxpdD1taW5zcGxpdCwgbWluYnVja2V0PW1pbmJ1Y2tldClcblxuIyBwcmludCBtb2RlbCBwcmVkaWN0aW9uIHJlc3VsdHNcbnJlc3VsdHNPdXRTUU1zMiRPT0JfUk1TRVxucmVzdWx0c091dFNRTXMyJE9PQl9NQUVcbnJlc3VsdHNPdXRTUU1zMiRSc3F1YXJlZFxuXG5gYGBcbmBgYFxuXG48IS0tIHJuYi1zb3VyY2UtZW5kIC0tPlxuIn0= -->


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```r
```r

nperm <- 5

resultsOutSQMs2 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSQMs2$OOB_RMSE
resultsOutSQMs2$OOB_MAE
resultsOutSQMs2$Rsquared

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Train multiple seeds model


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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resultsOutSQMs2 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSQMs2$OOB_RMSE
resultsOutSQMs2$OOB_MAE
resultsOutSQMs2$Rsquared



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


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```r
```r

resultsOutSQMs2 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSQMs2$OOB_RMSE
resultsOutSQMs2$OOB_MAE
resultsOutSQMs2$Rsquared

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->



<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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# store results
resdHiAnnoyFitAB['Diff SQMs no tonal loud', 'RMSE'] <- resultsOutSQMs2$OOB_RMSE
resdHiAnnoyFitAB['Diff SQMs no tonal loud', 'MAE'] <- resultsOutSQMs2$OOB_MAE
resdHiAnnoyFitAB['Diff SQMs no tonal loud', 'Rsquared'] <- resultsOutSQMs2$Rsquared
resdHiAnnoyPermImpAB$DiffSQMs2 <- resultsOutSQMs2$conditional_permimp



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


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```r
```r

# store results
resdHiAnnoyFitAB['Diff SQMs no tonal loud', 'RMSE'] <- resultsOutSQMs2$OOB_RMSE
resdHiAnnoyFitAB['Diff SQMs no tonal loud', 'MAE'] <- resultsOutSQMs2$OOB_MAE
resdHiAnnoyFitAB['Diff SQMs no tonal loud', 'Rsquared'] <- resultsOutSQMs2$Rsquared
resdHiAnnoyPermImpAB$DiffSQMs2 <- resultsOutSQMs2$conditional_permimp

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


###### Plot results


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutSQMs2.conimp <- arrange(resultsOutSQMs2$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutSQMs2.conimp) + geom_col(aes(x=factor(rownames(resultsOutSQMs2.conimp), levels=rownames(resultsOutSQMs2.conimp)), y=CondPermImp), fill=mycolours[7], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (% highly annoyed)\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 50))
pBar

if (saveplots){
  ggsave(filename=\PtsABdHiAnnoyDiffSQMsNoTonLdConPermimp.svg\, width=8, height=2.4, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdHiAnnoyDiffSQMsNoTonLdConPermimp.svg\)
  
  ggsave(filename=\PtsABdHiAnnoyDiffSQMsNoTonLdConPermimp.pdf\, width=8, height=2.4, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdHiAnnoyDiffSQMsNoTonLdConPermimp.pdf\)
}



<!-- rnb-output-end -->

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```r
```r
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutSQMs2.conimp <- arrange(resultsOutSQMs2$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutSQMs2.conimp) + geom_col(aes(x=factor(rownames(resultsOutSQMs2.conimp), levels=rownames(resultsOutSQMs2.conimp)), y=CondPermImp), fill=mycolours[7], width=0.5) + labs(x=\Variable\, y=\Conditional variable permutation importance (% highly annoyed)\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 50))
pBar

if (saveplots){
  ggsave(filename=\PtsABdHiAnnoyDiffSQMsNoTonLdConPermimp.svg\, width=8, height=2.4, path=file.path(outFigPath, \svg\))
  unlink(\PtsABdHiAnnoyDiffSQMsNoTonLdConPermimp.svg\)
  
  ggsave(filename=\PtsABdHiAnnoyDiffSQMsNoTonLdConPermimp.pdf\, width=8, height=2.4, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABdHiAnnoyDiffSQMsNoTonLdConPermimp.pdf\)
}

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


### Save the results outputs to file


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->

if (savedata){
  utils::write.csv(resdHiAnnoyFitAB, paste(outDataPath, \\\PtsABCRFdHiAnnoyOOBFit.csv\, sep=\\))
  ii <- 0
  temp = list()
  for (res in resdHiAnnoyPermImpAB){
    ii <- ii + 1
    temp[[ii]] <- as.data.frame(resdHiAnnoyPermImpAB[ii])
    names(temp[[ii]]) <- names(resdHiAnnoyPermImpAB[ii])
  }
  openxlsx::write.xlsx(temp, paste(outDataPath, \\\PtsABCRFdHiAnnoyConPermimp.xlsx\,
                                   sep=\\),
                       rowNames=TRUE)
}



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


<!-- rnb-source-begin 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 -->

```r
```r

if (savedata){
  utils::write.csv(resdHiAnnoyFitAB, paste(outDataPath, \\\PtsABCRFdHiAnnoyOOBFit.csv\, sep=\\))
  ii <- 0
  temp = list()
  for (res in resdHiAnnoyPermImpAB){
    ii <- ii + 1
    temp[[ii]] <- as.data.frame(resdHiAnnoyPermImpAB[ii])
    names(temp[[ii]]) <- names(resdHiAnnoyPermImpAB[ii])
  }
  openxlsx::write.xlsx(temp, paste(outDataPath, \\\PtsABCRFdHiAnnoyConPermimp.xlsx\,
                                   sep=\\),
                       rowNames=TRUE)
}

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->



## Parts A&B summary

Summary of results for Parts A & B combined

### With tonal loudness

#### Absolute variables


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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# combine the annoyance perm importance results

# convert each result to a tibble with rownames added to a column, renaming the data column to 'dAnnoy' etc.
resdAnnoyMnAbsPermImpTblAB <- as.data.frame(resdAnnoyMnPermImpAB$AbsSQMs1/max(resdAnnoyMnPermImpAB$AbsSQMs1)) |>
  tibble::rownames_to_column(var='Variable')
colnames(resdAnnoyMnAbsPermImpTblAB)[2] <- \dAnnoy\

resdHiAnnoyAbsPermImpTblAB <- as.data.frame(resdHiAnnoyPermImpAB$AbsSQMs1/max(resdHiAnnoyPermImpAB$AbsSQMs1)) |>
  tibble::rownames_to_column(var='Variable')
colnames(resdHiAnnoyAbsPermImpTblAB)[2] <- \dHiAnnoy\

# merge the dataframes
resAbsPermImpTblAB <- list(resdAnnoyMnAbsPermImpTblAB, resdHiAnnoyAbsPermImpTblAB) |>
  purrr::reduce(merge, by = c('Variable'), all = T)

# rename the columns
colnames(resAbsPermImpTblAB)[2:3] <- c(\Mean change in annoyance\, \%HA | HA' (amb.)\)
resAbsPermImpTblAB[is.na(resAbsPermImpTblAB)] <- 0

resAbsAB <- tidyr::pivot_longer(resAbsPermImpTblAB, cols=-Variable, names_to=\Outcome\, values_to=\Imp\)

# reorder res tibble, descending by the variable Imp grouped sum and create column with new group order as a factor
resAbsAB <- resAbsAB |> mutate(Variable_sum = sum(Imp), .by=Variable) |> arrange(desc(Variable_sum)) |> group_by(Variable_sum, Variable) |>
   mutate(Order = cur_group_id()) |> mutate(Order = as.factor(Order)) |> arrange(desc(Order))

# Reorder outcome levels
resAbsAB$Outcome <- factor(resAbsAB$Outcome, levels=c(\Mean change in annoyance\, \%HA | HA' (amb.)\))

# plot res as horizontal bar chart, with Imp as y axis, Variable as x axis, Outcome as fill, and Variable_sum as order, relabel x axis with Variable names
pBar <- ggplot(resAbsAB) + geom_col(aes(fill=Outcome, y=Imp, x=Order), colour='grey35', linewidth=0,  width=0.75, show.legend=TRUE) + labs(x=\Variable\, y=\Normalised conditional variable\npermutation importance\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 1)) + scale_fill_manual(values=mycolours, labels=c(expression(paste(bar(Delta~A))), \%HA | HA' (amb.)\)) + scale_x_discrete(labels=unique(rev(resAbsAB$Variable))) + guides(fill=guide_legend(title='Outcome'))
pBar + scale_y_continuous(breaks=seq(0, 1, by=0.5))



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```r
```r
# combine the annoyance perm importance results

# convert each result to a tibble with rownames added to a column, renaming the data column to 'dAnnoy' etc.
resdAnnoyMnAbsPermImpTblAB <- as.data.frame(resdAnnoyMnPermImpAB$AbsSQMs1/max(resdAnnoyMnPermImpAB$AbsSQMs1)) |>
  tibble::rownames_to_column(var='Variable')
colnames(resdAnnoyMnAbsPermImpTblAB)[2] <- \dAnnoy\

resdHiAnnoyAbsPermImpTblAB <- as.data.frame(resdHiAnnoyPermImpAB$AbsSQMs1/max(resdHiAnnoyPermImpAB$AbsSQMs1)) |>
  tibble::rownames_to_column(var='Variable')
colnames(resdHiAnnoyAbsPermImpTblAB)[2] <- \dHiAnnoy\

# merge the dataframes
resAbsPermImpTblAB <- list(resdAnnoyMnAbsPermImpTblAB, resdHiAnnoyAbsPermImpTblAB) |>
  purrr::reduce(merge, by = c('Variable'), all = T)

# rename the columns
colnames(resAbsPermImpTblAB)[2:3] <- c(\Mean change in annoyance\, \%HA | HA' (amb.)\)
resAbsPermImpTblAB[is.na(resAbsPermImpTblAB)] <- 0

resAbsAB <- tidyr::pivot_longer(resAbsPermImpTblAB, cols=-Variable, names_to=\Outcome\, values_to=\Imp\)

# reorder res tibble, descending by the variable Imp grouped sum and create column with new group order as a factor
resAbsAB <- resAbsAB |> mutate(Variable_sum = sum(Imp), .by=Variable) |> arrange(desc(Variable_sum)) |> group_by(Variable_sum, Variable) |>
   mutate(Order = cur_group_id()) |> mutate(Order = as.factor(Order)) |> arrange(desc(Order))

# Reorder outcome levels
resAbsAB$Outcome <- factor(resAbsAB$Outcome, levels=c(\Mean change in annoyance\, \%HA | HA' (amb.)\))

# plot res as horizontal bar chart, with Imp as y axis, Variable as x axis, Outcome as fill, and Variable_sum as order, relabel x axis with Variable names
pBar <- ggplot(resAbsAB) + geom_col(aes(fill=Outcome, y=Imp, x=Order), colour='grey35', linewidth=0,  width=0.75, show.legend=TRUE) + labs(x=\Variable\, y=\Normalised conditional variable\npermutation importance\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 1)) + scale_fill_manual(values=mycolours, labels=c(expression(paste(bar(Delta~A))), \%HA | HA' (amb.)\)) + scale_x_discrete(labels=unique(rev(resAbsAB$Variable))) + guides(fill=guide_legend(title='Outcome'))
pBar + scale_y_continuous(breaks=seq(0, 1, by=0.5))
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

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```

<!-- rnb-plot-begin eyJoZWlnaHQiOjQsIndpZHRoIjo0LCJzaXplX2JlaGF2aW9yIjoxLCJjb25kaXRpb25zIjpbXX0= -->

<img src=\data:image/png;base64
```



<!-- rnb-output-end -->

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/>\n\n<!-- rnb-plot-end -->\n"} -->


<!-- rnb-plot-begin -->

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" />

<!-- rnb-plot-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->

if (saveplots){
  ggsave(filename=\PtsABcrfAbsSQMsSummary.svg\, width=8, height=4, path=file.path(outFigPath, \svg\))
  unlink(\PtsABcrfAbsSQMsSummary.svg\)

  ggsave(filename=\PtsABcrfAbsSQMsSummary.pdf\, width=8, height=4, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABcrfAbsSQMsSummary.pdf\)
}


# plot res as horizontal bar chart, with Imp as y axis, Variable as x axis, Outcome as fill, and Variable_sum as order, relabel x axis with Variable names
pBar <- ggplot(resAbsAB) + geom_col(aes(fill=Outcome, y=Imp, x=Order), colour='grey35', linewidth=0,  width=0.75, show.legend=TRUE) + labs(x=\Variable\, y=\Normalised conditional variable\npermutation importance\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2), legend.position = \top\) + coord_flip(ylim=c(0, 1)) + scale_fill_manual(values=mycolours, labels=c(expression(paste(bar(Delta~A))), \%HA | HA' (amb.)\)) + scale_x_discrete(labels=unique(rev(resAbsAB$Variable))) + guides(fill=guide_legend(title='Outcome', nrow=2, ncol=1))
pBar + scale_y_continuous(breaks=seq(0, 1, by=0.5))



<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


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```r
```r

if (saveplots){
  ggsave(filename=\PtsABcrfAbsSQMsSummary.svg\, width=8, height=4, path=file.path(outFigPath, \svg\))
  unlink(\PtsABcrfAbsSQMsSummary.svg\)

  ggsave(filename=\PtsABcrfAbsSQMsSummary.pdf\, width=8, height=4, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABcrfAbsSQMsSummary.pdf\)
}


# plot res as horizontal bar chart, with Imp as y axis, Variable as x axis, Outcome as fill, and Variable_sum as order, relabel x axis with Variable names
pBar <- ggplot(resAbsAB) + geom_col(aes(fill=Outcome, y=Imp, x=Order), colour='grey35', linewidth=0,  width=0.75, show.legend=TRUE) + labs(x=\Variable\, y=\Normalised conditional variable\npermutation importance\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2), legend.position = \top\) + coord_flip(ylim=c(0, 1)) + scale_fill_manual(values=mycolours, labels=c(expression(paste(bar(Delta~A))), \%HA | HA' (amb.)\)) + scale_x_discrete(labels=unique(rev(resAbsAB$Variable))) + guides(fill=guide_legend(title='Outcome', nrow=2, ncol=1))
pBar + scale_y_continuous(breaks=seq(0, 1, by=0.5))
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

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```

<!-- rnb-plot-begin eyJoZWlnaHQiOjQsIndpZHRoIjo0LCJzaXplX2JlaGF2aW9yIjoxLCJjb25kaXRpb25zIjpbXX0= -->

<img src=\data:image/png;base64
```



<!-- rnb-output-end -->

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" />

<!-- rnb-plot-end -->


<!-- rnb-output-end -->

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if (saveplots){
  ggsave(filename=\PtsABcrfAbsSQMsSummaryNw.svg\, width=4, height=4, path=file.path(outFigPath, \svg\))
  unlink(\PtsABcrfAbsSQMsSummary.svg\)

  ggsave(filename=\PtsABcrfAbsSQMsSummaryNw.pdf\, width=4, height=4, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABcrfAbsSQMsSummary.pdf\)
}



<!-- rnb-output-end -->

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```r
```r

if (saveplots){
  ggsave(filename=\PtsABcrfAbsSQMsSummaryNw.svg\, width=4, height=4, path=file.path(outFigPath, \svg\))
  unlink(\PtsABcrfAbsSQMsSummary.svg\)

  ggsave(filename=\PtsABcrfAbsSQMsSummaryNw.pdf\, width=4, height=4, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABcrfAbsSQMsSummary.pdf\)
}

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->

#### Difference variables


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n# combine the annoyance perm importance results\n\n# convert each result to a tibble with rownames added to a column, renaming the data column to 'dAnnoy' etc.\nresdAnnoyMnDiffPermImpTblAB <- as.data.frame(resdAnnoyMnPermImpAB$DiffSQMs1/max(resdAnnoyMnPermImpAB$DiffSQMs1)) |>\n  tibble::rownames_to_column(var='Variable')\ncolnames(resdAnnoyMnDiffPermImpTblAB)[2] <- \\dAnnoy\\\n\nresdHiAnnoyDiffPermImpTblAB <- as.data.frame(resdHiAnnoyPermImpAB$DiffSQMs1/max(resdHiAnnoyPermImpAB$DiffSQMs1)) |>\n  tibble::rownames_to_column(var='Variable')\ncolnames(resdHiAnnoyDiffPermImpTblAB)[2] <- \\dHiAnnoy\\\n\n# merge the dataframes\nresDiffPermImpTblAB <- list(resdAnnoyMnDiffPermImpTblAB, resdHiAnnoyDiffPermImpTblAB) |>\n  purrr::reduce(merge, by = c('Variable'), all = T)\n\n# rename the columns\ncolnames(resDiffPermImpTblAB)[2:3] <- c(\\Mean change in annoyance\\, \\%HA | HA' (amb.)\\)\nresDiffPermImpTblAB[is.na(resDiffPermImpTblAB)] <- 0\n\nresDiffAB <- tidyr::pivot_longer(resDiffPermImpTblAB, cols=-Variable, names_to=\\Outcome\\, values_to=\\Imp\\)\n\n# reorder res tibble, descending by the variable Imp grouped sum and create column with new group order as a factor\nresDiffAB <- resDiffAB |> mutate(Variable_sum = sum(Imp), .by=Variable) |> arrange(desc(Variable_sum)) |> group_by(Variable_sum, Variable) |>\n   mutate(Order = cur_group_id()) |> mutate(Order = as.factor(Order)) |> arrange(desc(Order))\n\n# Reorder outcome levels\nresDiffAB$Outcome <- factor(resDiffAB$Outcome, levels=c(\\Mean change in annoyance\\, \\%HA | HA' (amb.)\\))\n\n# plot res as horizontal bar chart, with Imp as y axis, Variable as x axis, Outcome as fill, and Variable_sum as order, relabel x axis with Variable names\npBar <- ggplot(resDiffAB) + geom_col(aes(fill=Outcome, y=Imp, x=Order), colour='grey35', linewidth=0,  width=0.75, show.legend=TRUE) + labs(x=\\Variable\\, y=\\Normalised conditional variable permutation importance\\) + theme(text = element_text(family = \\serif\\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2), legend.position = \\right\\) + coord_flip(ylim=c(-0.1, 1.1)) + scale_fill_manual(values=mycolours) + scale_x_discrete(labels=unique(rev(resDiffAB$Variable)))\npBar + scale_y_continuous(breaks=seq(0, 1, by=0.5))\n\nif (saveplots){\n  ggsave(filename=\\PtsABcrfDiffSQMsSummary.svg\\, width=8, height=3, path=file.path(outFigPath, \\svg\\))\n  unlink(\\PtsABcrfDiffSQMsSummary.svg\\)\n  \n  ggsave(filename=\\PtsABcrfDiffSQMsSummary.pdf\\, width=8, height=3, path=file.path(outFigPath, \\pdf\\))\n  unlink(\\PtsABcrfDiffSQMsSummary.pdf\\)\n}\n\n```\n\n<!-- rnb-source-end -->\n"} -->
# combine the annoyance perm importance results

# convert each result to a tibble with rownames added to a column, renaming the data column to 'dAnnoy' etc.
resdAnnoyMnDiffPermImpTblAB <- as.data.frame(resdAnnoyMnPermImpAB$DiffSQMs1/max(resdAnnoyMnPermImpAB$DiffSQMs1)) |>
  tibble::rownames_to_column(var='Variable')
colnames(resdAnnoyMnDiffPermImpTblAB)[2] <- \dAnnoy\

resdHiAnnoyDiffPermImpTblAB <- as.data.frame(resdHiAnnoyPermImpAB$DiffSQMs1/max(resdHiAnnoyPermImpAB$DiffSQMs1)) |>
  tibble::rownames_to_column(var='Variable')
colnames(resdHiAnnoyDiffPermImpTblAB)[2] <- \dHiAnnoy\

# merge the dataframes
resDiffPermImpTblAB <- list(resdAnnoyMnDiffPermImpTblAB, resdHiAnnoyDiffPermImpTblAB) |>
  purrr::reduce(merge, by = c('Variable'), all = T)

# rename the columns
colnames(resDiffPermImpTblAB)[2:3] <- c(\Mean change in annoyance\, \%HA | HA' (amb.)\)
resDiffPermImpTblAB[is.na(resDiffPermImpTblAB)] <- 0

resDiffAB <- tidyr::pivot_longer(resDiffPermImpTblAB, cols=-Variable, names_to=\Outcome\, values_to=\Imp\)

# reorder res tibble, descending by the variable Imp grouped sum and create column with new group order as a factor
resDiffAB <- resDiffAB |> mutate(Variable_sum = sum(Imp), .by=Variable) |> arrange(desc(Variable_sum)) |> group_by(Variable_sum, Variable) |>
   mutate(Order = cur_group_id()) |> mutate(Order = as.factor(Order)) |> arrange(desc(Order))

# Reorder outcome levels
resDiffAB$Outcome <- factor(resDiffAB$Outcome, levels=c(\Mean change in annoyance\, \%HA | HA' (amb.)\))

# plot res as horizontal bar chart, with Imp as y axis, Variable as x axis, Outcome as fill, and Variable_sum as order, relabel x axis with Variable names
pBar <- ggplot(resDiffAB) + geom_col(aes(fill=Outcome, y=Imp, x=Order), colour='grey35', linewidth=0,  width=0.75, show.legend=TRUE) + labs(x=\Variable\, y=\Normalised conditional variable permutation importance\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2), legend.position = \right\) + coord_flip(ylim=c(-0.1, 1.1)) + scale_fill_manual(values=mycolours) + scale_x_discrete(labels=unique(rev(resDiffAB$Variable)))
pBar + scale_y_continuous(breaks=seq(0, 1, by=0.5))

if (saveplots){
  ggsave(filename=\PtsABcrfDiffSQMsSummary.svg\, width=8, height=3, path=file.path(outFigPath, \svg\))
  unlink(\PtsABcrfDiffSQMsSummary.svg\)
  
  ggsave(filename=\PtsABcrfDiffSQMsSummary.pdf\, width=8, height=3, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABcrfDiffSQMsSummary.pdf\)
}



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-->\n\n```r\n```r\n# combine the annoyance perm importance results\n\n# convert each result to a tibble with rownames added to a column, renaming the data column to 'dAnnoy' etc.\nresdAnnoyMnDiffPermImpTblAB <- as.data.frame(resdAnnoyMnPermImpAB$DiffSQMs1/max(resdAnnoyMnPermImpAB$DiffSQMs1)) |>\n  tibble::rownames_to_column(var='Variable')\ncolnames(resdAnnoyMnDiffPermImpTblAB)[2] <- \\dAnnoy\\\n\nresdHiAnnoyDiffPermImpTblAB <- as.data.frame(resdHiAnnoyPermImpAB$DiffSQMs1/max(resdHiAnnoyPermImpAB$DiffSQMs1)) |>\n  tibble::rownames_to_column(var='Variable')\ncolnames(resdHiAnnoyDiffPermImpTblAB)[2] <- \\dHiAnnoy\\\n\n# merge the dataframes\nresDiffPermImpTblAB <- list(resdAnnoyMnDiffPermImpTblAB, resdHiAnnoyDiffPermImpTblAB) |>\n  purrr::reduce(merge, by = c('Variable'), all = T)\n\n# rename the columns\ncolnames(resDiffPermImpTblAB)[2:3] <- c(\\Mean change in annoyance\\, \\%HA | HA' (amb.)\\)\nresDiffPermImpTblAB[is.na(resDiffPermImpTblAB)] <- 0\n\nresDiffAB <- tidyr::pivot_longer(resDiffPermImpTblAB, cols=-Variable, names_to=\\Outcome\\, values_to=\\Imp\\)\n\n# reorder res tibble, descending by the variable Imp grouped sum and create column with new group order as a factor\nresDiffAB <- resDiffAB |> mutate(Variable_sum = sum(Imp), .by=Variable) |> arrange(desc(Variable_sum)) |> group_by(Variable_sum, Variable) |>\n   mutate(Order = cur_group_id()) |> mutate(Order = as.factor(Order)) |> arrange(desc(Order))\n\n# Reorder outcome levels\nresDiffAB$Outcome <- factor(resDiffAB$Outcome, levels=c(\\Mean change in annoyance\\, \\%HA | HA' (amb.)\\))\n\n# plot res as horizontal bar chart, with Imp as y axis, Variable as x axis, Outcome as fill, and Variable_sum as order, relabel x axis with Variable names\npBar <- ggplot(resDiffAB) + geom_col(aes(fill=Outcome, y=Imp, x=Order), colour='grey35', linewidth=0,  width=0.75, show.legend=TRUE) + labs(x=\\Variable\\, y=\\Normalised conditional variable permutation importance\\) + theme(text = element_text(family = \\serif\\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2), legend.position = \\right\\) + coord_flip(ylim=c(-0.1, 1.1)) + scale_fill_manual(values=mycolours) + scale_x_discrete(labels=unique(rev(resDiffAB$Variable)))\npBar + scale_y_continuous(breaks=seq(0, 1, by=0.5))\n\nif (saveplots){\n  ggsave(filename=\\PtsABcrfDiffSQMsSummary.svg\\, width=8, height=3, path=file.path(outFigPath, \\svg\\))\n  unlink(\\PtsABcrfDiffSQMsSummary.svg\\)\n  \n  ggsave(filename=\\PtsABcrfDiffSQMsSummary.pdf\\, width=8, height=3, path=file.path(outFigPath, \\pdf\\))\n  unlink(\\PtsABcrfDiffSQMsSummary.pdf\\)\n}\n\n```\n```\n\n<!-- rnb-source-end -->\n"} -->


<!-- rnb-source-begin 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 -->

```r
```r
# combine the annoyance perm importance results

# convert each result to a tibble with rownames added to a column, renaming the data column to 'dAnnoy' etc.
resdAnnoyMnDiffPermImpTblAB <- as.data.frame(resdAnnoyMnPermImpAB$DiffSQMs1/max(resdAnnoyMnPermImpAB$DiffSQMs1)) |>
  tibble::rownames_to_column(var='Variable')
colnames(resdAnnoyMnDiffPermImpTblAB)[2] <- \dAnnoy\

resdHiAnnoyDiffPermImpTblAB <- as.data.frame(resdHiAnnoyPermImpAB$DiffSQMs1/max(resdHiAnnoyPermImpAB$DiffSQMs1)) |>
  tibble::rownames_to_column(var='Variable')
colnames(resdHiAnnoyDiffPermImpTblAB)[2] <- \dHiAnnoy\

# merge the dataframes
resDiffPermImpTblAB <- list(resdAnnoyMnDiffPermImpTblAB, resdHiAnnoyDiffPermImpTblAB) |>
  purrr::reduce(merge, by = c('Variable'), all = T)

# rename the columns
colnames(resDiffPermImpTblAB)[2:3] <- c(\Mean change in annoyance\, \%HA | HA' (amb.)\)
resDiffPermImpTblAB[is.na(resDiffPermImpTblAB)] <- 0

resDiffAB <- tidyr::pivot_longer(resDiffPermImpTblAB, cols=-Variable, names_to=\Outcome\, values_to=\Imp\)

# reorder res tibble, descending by the variable Imp grouped sum and create column with new group order as a factor
resDiffAB <- resDiffAB |> mutate(Variable_sum = sum(Imp), .by=Variable) |> arrange(desc(Variable_sum)) |> group_by(Variable_sum, Variable) |>
   mutate(Order = cur_group_id()) |> mutate(Order = as.factor(Order)) |> arrange(desc(Order))

# Reorder outcome levels
resDiffAB$Outcome <- factor(resDiffAB$Outcome, levels=c(\Mean change in annoyance\, \%HA | HA' (amb.)\))

# plot res as horizontal bar chart, with Imp as y axis, Variable as x axis, Outcome as fill, and Variable_sum as order, relabel x axis with Variable names
pBar <- ggplot(resDiffAB) + geom_col(aes(fill=Outcome, y=Imp, x=Order), colour='grey35', linewidth=0,  width=0.75, show.legend=TRUE) + labs(x=\Variable\, y=\Normalised conditional variable permutation importance\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2), legend.position = \right\) + coord_flip(ylim=c(-0.1, 1.1)) + scale_fill_manual(values=mycolours) + scale_x_discrete(labels=unique(rev(resDiffAB$Variable)))
pBar + scale_y_continuous(breaks=seq(0, 1, by=0.5))

if (saveplots){
  ggsave(filename=\PtsABcrfDiffSQMsSummary.svg\, width=8, height=3, path=file.path(outFigPath, \svg\))
  unlink(\PtsABcrfDiffSQMsSummary.svg\)
  
  ggsave(filename=\PtsABcrfDiffSQMsSummary.pdf\, width=8, height=3, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABcrfDiffSQMsSummary.pdf\)
}

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


### No tonal loudness

#### Absolute variables


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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 -->\n\n```r\n# combine the annoyance perm importance results\n\n# convert each result to a tibble with rownames added to a column, renaming the data column to 'dAnnoy' etc.\nresdAnnoyMnAbsPermImpNoTonLdTblAB <- as.data.frame(resdAnnoyMnPermImpAB$AbsSQMs2/max(resdAnnoyMnPermImpAB$AbsSQMs2)) |>\n  tibble::rownames_to_column(var='Variable')\ncolnames(resdAnnoyMnAbsPermImpNoTonLdTblAB)[2] <- \\dAnnoy\\\n\nresdHiAnnoyAbsPermImpNoTonLdTblAB <- as.data.frame(resdHiAnnoyPermImpAB$AbsSQMs2/max(resdHiAnnoyPermImpAB$AbsSQMs2)) |>\n  tibble::rownames_to_column(var='Variable')\ncolnames(resdHiAnnoyAbsPermImpNoTonLdTblAB)[2] <- \\dHiAnnoy\\\n\n# merge the dataframes\nresAbsPermImpNoTonLdTblAB <- list(resdAnnoyMnAbsPermImpNoTonLdTblAB, resdHiAnnoyAbsPermImpNoTonLdTblAB) |>\n  purrr::reduce(merge, by = c('Variable'), all = T)\n\n# rename the columns\ncolnames(resAbsPermImpNoTonLdTblAB)[2:3] <- c(\\Mean change in annoyance\\, \\%HA | HA' (amb.)\\)\nresAbsPermImpNoTonLdTblAB[is.na(resAbsPermImpNoTonLdTblAB)] <- 0\n\nresAbsNoTonLdAB <- tidyr::pivot_longer(resAbsPermImpNoTonLdTblAB, cols=-Variable, names_to=\\Outcome\\, values_to=\\Imp\\)\n\n# reorder res tibble, descending by the variable Imp grouped sum and create column with new group order as a factor\nresAbsNoTonLdAB <- resAbsNoTonLdAB |> mutate(Variable_sum = sum(Imp), .by=Variable) |> arrange(desc(Variable_sum)) |> group_by(Variable_sum, Variable) |>\n   mutate(Order = cur_group_id()) |> mutate(Order = as.factor(Order)) |> arrange(desc(Order))\n\n# Reorder outcome levels\nresAbsNoTonLdAB$Outcome <- factor(resAbsNoTonLdAB$Outcome, levels=c(\\Mean change in annoyance\\, \\%HA | HA' (amb.)\\))\n\n# plot res as horizontal bar chart, with Imp as y axis, Variable as x axis, Outcome as fill, and Variable_sum as order, relabel x axis with Variable names\npBar <- ggplot(resAbsNoTonLdAB) + geom_col(aes(fill=Outcome, y=Imp, x=Order), colour='grey35', linewidth=0,  width=0.75, show.legend=TRUE) + labs(x=\\Variable\\, y=\\Normalised conditional variable permutation importance\\) + theme(text = element_text(family = \\serif\\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2), legend.position = \\right\\) + coord_flip(ylim=c(-0.1, 1.1)) + scale_fill_manual(values=mycolours) + scale_x_discrete(labels=unique(rev(resAbsNoTonLdAB$Variable)))\npBar + scale_y_continuous(breaks=seq(0, 1, by=0.5))\n\nif (saveplots){\n  ggsave(filename=\\PtsABcrfAbsSQMsNoTonLdSummary.svg\\, width=8, height=3, path=file.path(outFigPath, \\svg\\))\n  unlink(\\PtsABcrfAbsSQMsNoTonLdSummary.svg\\)\n  \n  ggsave(filename=\\PtsABcrfAbsSQMsNoTonLdSummary.pdf\\, width=8, height=3, path=file.path(outFigPath, \\pdf\\))\n  unlink(\\PtsABcrfAbsSQMsNoTonLdSummary.pdf\\)\n}\n\n```\n\n<!-- rnb-source-end -->\n"} -->
# combine the annoyance perm importance results

# convert each result to a tibble with rownames added to a column, renaming the data column to 'dAnnoy' etc.
resdAnnoyMnAbsPermImpNoTonLdTblAB <- as.data.frame(resdAnnoyMnPermImpAB$AbsSQMs2/max(resdAnnoyMnPermImpAB$AbsSQMs2)) |>
  tibble::rownames_to_column(var='Variable')
colnames(resdAnnoyMnAbsPermImpNoTonLdTblAB)[2] <- \dAnnoy\

resdHiAnnoyAbsPermImpNoTonLdTblAB <- as.data.frame(resdHiAnnoyPermImpAB$AbsSQMs2/max(resdHiAnnoyPermImpAB$AbsSQMs2)) |>
  tibble::rownames_to_column(var='Variable')
colnames(resdHiAnnoyAbsPermImpNoTonLdTblAB)[2] <- \dHiAnnoy\

# merge the dataframes
resAbsPermImpNoTonLdTblAB <- list(resdAnnoyMnAbsPermImpNoTonLdTblAB, resdHiAnnoyAbsPermImpNoTonLdTblAB) |>
  purrr::reduce(merge, by = c('Variable'), all = T)

# rename the columns
colnames(resAbsPermImpNoTonLdTblAB)[2:3] <- c(\Mean change in annoyance\, \%HA | HA' (amb.)\)
resAbsPermImpNoTonLdTblAB[is.na(resAbsPermImpNoTonLdTblAB)] <- 0

resAbsNoTonLdAB <- tidyr::pivot_longer(resAbsPermImpNoTonLdTblAB, cols=-Variable, names_to=\Outcome\, values_to=\Imp\)

# reorder res tibble, descending by the variable Imp grouped sum and create column with new group order as a factor
resAbsNoTonLdAB <- resAbsNoTonLdAB |> mutate(Variable_sum = sum(Imp), .by=Variable) |> arrange(desc(Variable_sum)) |> group_by(Variable_sum, Variable) |>
   mutate(Order = cur_group_id()) |> mutate(Order = as.factor(Order)) |> arrange(desc(Order))

# Reorder outcome levels
resAbsNoTonLdAB$Outcome <- factor(resAbsNoTonLdAB$Outcome, levels=c(\Mean change in annoyance\, \%HA | HA' (amb.)\))

# plot res as horizontal bar chart, with Imp as y axis, Variable as x axis, Outcome as fill, and Variable_sum as order, relabel x axis with Variable names
pBar <- ggplot(resAbsNoTonLdAB) + geom_col(aes(fill=Outcome, y=Imp, x=Order), colour='grey35', linewidth=0,  width=0.75, show.legend=TRUE) + labs(x=\Variable\, y=\Normalised conditional variable permutation importance\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2), legend.position = \right\) + coord_flip(ylim=c(-0.1, 1.1)) + scale_fill_manual(values=mycolours) + scale_x_discrete(labels=unique(rev(resAbsNoTonLdAB$Variable)))
pBar + scale_y_continuous(breaks=seq(0, 1, by=0.5))

if (saveplots){
  ggsave(filename=\PtsABcrfAbsSQMsNoTonLdSummary.svg\, width=8, height=3, path=file.path(outFigPath, \svg\))
  unlink(\PtsABcrfAbsSQMsNoTonLdSummary.svg\)
  
  ggsave(filename=\PtsABcrfAbsSQMsNoTonLdSummary.pdf\, width=8, height=3, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABcrfAbsSQMsNoTonLdSummary.pdf\)
}



<!-- rnb-output-end -->

<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n```r\n# combine the annoyance perm importance results\n\n# convert each result to a tibble with rownames added to a column, renaming the data column to 'dAnnoy' etc.\nresdAnnoyMnAbsPermImpNoTonLdTblAB <- as.data.frame(resdAnnoyMnPermImpAB$AbsSQMs2/max(resdAnnoyMnPermImpAB$AbsSQMs2)) |>\n  tibble::rownames_to_column(var='Variable')\ncolnames(resdAnnoyMnAbsPermImpNoTonLdTblAB)[2] <- \\dAnnoy\\\n\nresdHiAnnoyAbsPermImpNoTonLdTblAB <- as.data.frame(resdHiAnnoyPermImpAB$AbsSQMs2/max(resdHiAnnoyPermImpAB$AbsSQMs2)) |>\n  tibble::rownames_to_column(var='Variable')\ncolnames(resdHiAnnoyAbsPermImpNoTonLdTblAB)[2] <- \\dHiAnnoy\\\n\n# merge the dataframes\nresAbsPermImpNoTonLdTblAB <- list(resdAnnoyMnAbsPermImpNoTonLdTblAB, resdHiAnnoyAbsPermImpNoTonLdTblAB) |>\n  purrr::reduce(merge, by = c('Variable'), all = T)\n\n# rename the columns\ncolnames(resAbsPermImpNoTonLdTblAB)[2:3] <- c(\\Mean change in annoyance\\, \\%HA | HA' (amb.)\\)\nresAbsPermImpNoTonLdTblAB[is.na(resAbsPermImpNoTonLdTblAB)] <- 0\n\nresAbsNoTonLdAB <- tidyr::pivot_longer(resAbsPermImpNoTonLdTblAB, cols=-Variable, names_to=\\Outcome\\, values_to=\\Imp\\)\n\n# reorder res tibble, descending by the variable Imp grouped sum and create column with new group order as a factor\nresAbsNoTonLdAB <- resAbsNoTonLdAB |> mutate(Variable_sum = sum(Imp), .by=Variable) |> arrange(desc(Variable_sum)) |> group_by(Variable_sum, Variable) |>\n   mutate(Order = cur_group_id()) |> mutate(Order = as.factor(Order)) |> arrange(desc(Order))\n\n# Reorder outcome levels\nresAbsNoTonLdAB$Outcome <- factor(resAbsNoTonLdAB$Outcome, levels=c(\\Mean change in annoyance\\, \\%HA | HA' (amb.)\\))\n\n# plot res as horizontal bar chart, with Imp as y axis, Variable as x axis, Outcome as fill, and Variable_sum as order, relabel x axis with Variable names\npBar <- ggplot(resAbsNoTonLdAB) + geom_col(aes(fill=Outcome, y=Imp, x=Order), colour='grey35', linewidth=0,  width=0.75, show.legend=TRUE) + labs(x=\\Variable\\, y=\\Normalised conditional variable permutation importance\\) + theme(text = element_text(family = \\serif\\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2), legend.position = \\right\\) + coord_flip(ylim=c(-0.1, 1.1)) + scale_fill_manual(values=mycolours) + scale_x_discrete(labels=unique(rev(resAbsNoTonLdAB$Variable)))\npBar + scale_y_continuous(breaks=seq(0, 1, by=0.5))\n\nif (saveplots){\n  ggsave(filename=\\PtsABcrfAbsSQMsNoTonLdSummary.svg\\, width=8, height=3, path=file.path(outFigPath, \\svg\\))\n  unlink(\\PtsABcrfAbsSQMsNoTonLdSummary.svg\\)\n  \n  ggsave(filename=\\PtsABcrfAbsSQMsNoTonLdSummary.pdf\\, width=8, height=3, path=file.path(outFigPath, \\pdf\\))\n  unlink(\\PtsABcrfAbsSQMsNoTonLdSummary.pdf\\)\n}\n\n```\n```\n\n<!-- rnb-source-end -->\n"} -->


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```r
```r
# combine the annoyance perm importance results

# convert each result to a tibble with rownames added to a column, renaming the data column to 'dAnnoy' etc.
resdAnnoyMnAbsPermImpNoTonLdTblAB <- as.data.frame(resdAnnoyMnPermImpAB$AbsSQMs2/max(resdAnnoyMnPermImpAB$AbsSQMs2)) |>
  tibble::rownames_to_column(var='Variable')
colnames(resdAnnoyMnAbsPermImpNoTonLdTblAB)[2] <- \dAnnoy\

resdHiAnnoyAbsPermImpNoTonLdTblAB <- as.data.frame(resdHiAnnoyPermImpAB$AbsSQMs2/max(resdHiAnnoyPermImpAB$AbsSQMs2)) |>
  tibble::rownames_to_column(var='Variable')
colnames(resdHiAnnoyAbsPermImpNoTonLdTblAB)[2] <- \dHiAnnoy\

# merge the dataframes
resAbsPermImpNoTonLdTblAB <- list(resdAnnoyMnAbsPermImpNoTonLdTblAB, resdHiAnnoyAbsPermImpNoTonLdTblAB) |>
  purrr::reduce(merge, by = c('Variable'), all = T)

# rename the columns
colnames(resAbsPermImpNoTonLdTblAB)[2:3] <- c(\Mean change in annoyance\, \%HA | HA' (amb.)\)
resAbsPermImpNoTonLdTblAB[is.na(resAbsPermImpNoTonLdTblAB)] <- 0

resAbsNoTonLdAB <- tidyr::pivot_longer(resAbsPermImpNoTonLdTblAB, cols=-Variable, names_to=\Outcome\, values_to=\Imp\)

# reorder res tibble, descending by the variable Imp grouped sum and create column with new group order as a factor
resAbsNoTonLdAB <- resAbsNoTonLdAB |> mutate(Variable_sum = sum(Imp), .by=Variable) |> arrange(desc(Variable_sum)) |> group_by(Variable_sum, Variable) |>
   mutate(Order = cur_group_id()) |> mutate(Order = as.factor(Order)) |> arrange(desc(Order))

# Reorder outcome levels
resAbsNoTonLdAB$Outcome <- factor(resAbsNoTonLdAB$Outcome, levels=c(\Mean change in annoyance\, \%HA | HA' (amb.)\))

# plot res as horizontal bar chart, with Imp as y axis, Variable as x axis, Outcome as fill, and Variable_sum as order, relabel x axis with Variable names
pBar <- ggplot(resAbsNoTonLdAB) + geom_col(aes(fill=Outcome, y=Imp, x=Order), colour='grey35', linewidth=0,  width=0.75, show.legend=TRUE) + labs(x=\Variable\, y=\Normalised conditional variable permutation importance\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2), legend.position = \right\) + coord_flip(ylim=c(-0.1, 1.1)) + scale_fill_manual(values=mycolours) + scale_x_discrete(labels=unique(rev(resAbsNoTonLdAB$Variable)))
pBar + scale_y_continuous(breaks=seq(0, 1, by=0.5))

if (saveplots){
  ggsave(filename=\PtsABcrfAbsSQMsNoTonLdSummary.svg\, width=8, height=3, path=file.path(outFigPath, \svg\))
  unlink(\PtsABcrfAbsSQMsNoTonLdSummary.svg\)
  
  ggsave(filename=\PtsABcrfAbsSQMsNoTonLdSummary.pdf\, width=8, height=3, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABcrfAbsSQMsNoTonLdSummary.pdf\)
}

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->

#### Difference variables


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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-->\n\n```r\n# combine the annoyance perm importance results\n\n# convert each result to a tibble with rownames added to a column, renaming the data column to 'dAnnoy' etc.\nresdAnnoyMnDiffPermImpNoTonLdTblAB <- as.data.frame(resdAnnoyMnPermImpAB$DiffSQMs2/max(resdAnnoyMnPermImpAB$DiffSQMs2)) |>\n  tibble::rownames_to_column(var='Variable')\ncolnames(resdAnnoyMnDiffPermImpNoTonLdTblAB)[2] <- \\dAnnoy\\\n\nresdHiAnnoyDiffPermImpNoTonLdTblAB <- as.data.frame(resdHiAnnoyPermImpAB$DiffSQMs2/max(resdHiAnnoyPermImpAB$DiffSQMs2)) |>\n  tibble::rownames_to_column(var='Variable')\ncolnames(resdHiAnnoyDiffPermImpNoTonLdTblAB)[2] <- \\dHiAnnoy\\\n\n# merge the dataframes\nresDiffPermImpNoTonLdTblAB <- list(resdAnnoyMnDiffPermImpNoTonLdTblAB, resdHiAnnoyDiffPermImpNoTonLdTblAB) |>\n  purrr::reduce(merge, by = c('Variable'), all = T)\n\n# rename the columns\ncolnames(resDiffPermImpNoTonLdTblAB)[2:3] <- c(\\Mean change in annoyance\\, \\%HA | HA' (amb.)\\)\nresDiffPermImpNoTonLdTblAB[is.na(resDiffPermImpNoTonLdTblAB)] <- 0\n\nresDiffNoTonLdAB <- tidyr::pivot_longer(resDiffPermImpNoTonLdTblAB, cols=-Variable, names_to=\\Outcome\\, values_to=\\Imp\\)\n\n# reorder res tibble, descending by the variable Imp grouped sum and create column with new group order as a factor\nresDiffNoTonLdAB <- resDiffNoTonLdAB |> mutate(Variable_sum = sum(Imp), .by=Variable) |> arrange(desc(Variable_sum)) |> group_by(Variable_sum, Variable) |>\n   mutate(Order = cur_group_id()) |> mutate(Order = as.factor(Order)) |> arrange(desc(Order))\n\n# Reorder outcome levels\nresDiffNoTonLdAB$Outcome <- factor(resDiffNoTonLdAB$Outcome, levels=c(\\Mean change in annoyance\\, \\%HA | HA' (amb.)\\))\n\n# plot res as horizontal bar chart, with Imp as y axis, Variable as x axis, Outcome as fill, and Variable_sum as order, relabel x axis with Variable names\npBar <- ggplot(resDiffNoTonLdAB) + geom_col(aes(fill=Outcome, y=Imp, x=Order), colour='grey35', linewidth=0,  width=0.75, show.legend=TRUE) + labs(x=\\Variable\\, y=\\Normalised conditional variable permutation importance\\) + theme(text = element_text(family = \\serif\\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2), legend.position = \\right\\) + coord_flip(ylim=c(-0.1, 1.1)) + scale_fill_manual(values=mycolours) + scale_x_discrete(labels=unique(rev(resDiffNoTonLdAB$Variable)))\npBar + scale_y_continuous(breaks=seq(0, 1, by=0.5))\n\nif (saveplots){\n  ggsave(filename=\\PtsABcrfDiffSQMsNoTonLdSummary.svg\\, width=8, height=3, path=file.path(outFigPath, \\svg\\))\n  unlink(\\PtsABcrfDiffSQMsNoTonLdSummary.svg\\)\n  \n  ggsave(filename=\\PtsABcrfDiffSQMsNoTonLdSummary.pdf\\, width=8, height=3, path=file.path(outFigPath, \\pdf\\))\n  unlink(\\PtsABcrfDiffSQMsNoTonLdSummary.pdf\\)\n}\n\n```\n\n<!-- rnb-source-end -->\n"} -->
# combine the annoyance perm importance results

# convert each result to a tibble with rownames added to a column, renaming the data column to 'dAnnoy' etc.
resdAnnoyMnDiffPermImpNoTonLdTblAB <- as.data.frame(resdAnnoyMnPermImpAB$DiffSQMs2/max(resdAnnoyMnPermImpAB$DiffSQMs2)) |>
  tibble::rownames_to_column(var='Variable')
colnames(resdAnnoyMnDiffPermImpNoTonLdTblAB)[2] <- \dAnnoy\

resdHiAnnoyDiffPermImpNoTonLdTblAB <- as.data.frame(resdHiAnnoyPermImpAB$DiffSQMs2/max(resdHiAnnoyPermImpAB$DiffSQMs2)) |>
  tibble::rownames_to_column(var='Variable')
colnames(resdHiAnnoyDiffPermImpNoTonLdTblAB)[2] <- \dHiAnnoy\

# merge the dataframes
resDiffPermImpNoTonLdTblAB <- list(resdAnnoyMnDiffPermImpNoTonLdTblAB, resdHiAnnoyDiffPermImpNoTonLdTblAB) |>
  purrr::reduce(merge, by = c('Variable'), all = T)

# rename the columns
colnames(resDiffPermImpNoTonLdTblAB)[2:3] <- c(\Mean change in annoyance\, \%HA | HA' (amb.)\)
resDiffPermImpNoTonLdTblAB[is.na(resDiffPermImpNoTonLdTblAB)] <- 0

resDiffNoTonLdAB <- tidyr::pivot_longer(resDiffPermImpNoTonLdTblAB, cols=-Variable, names_to=\Outcome\, values_to=\Imp\)

# reorder res tibble, descending by the variable Imp grouped sum and create column with new group order as a factor
resDiffNoTonLdAB <- resDiffNoTonLdAB |> mutate(Variable_sum = sum(Imp), .by=Variable) |> arrange(desc(Variable_sum)) |> group_by(Variable_sum, Variable) |>
   mutate(Order = cur_group_id()) |> mutate(Order = as.factor(Order)) |> arrange(desc(Order))

# Reorder outcome levels
resDiffNoTonLdAB$Outcome <- factor(resDiffNoTonLdAB$Outcome, levels=c(\Mean change in annoyance\, \%HA | HA' (amb.)\))

# plot res as horizontal bar chart, with Imp as y axis, Variable as x axis, Outcome as fill, and Variable_sum as order, relabel x axis with Variable names
pBar <- ggplot(resDiffNoTonLdAB) + geom_col(aes(fill=Outcome, y=Imp, x=Order), colour='grey35', linewidth=0,  width=0.75, show.legend=TRUE) + labs(x=\Variable\, y=\Normalised conditional variable permutation importance\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2), legend.position = \right\) + coord_flip(ylim=c(-0.1, 1.1)) + scale_fill_manual(values=mycolours) + scale_x_discrete(labels=unique(rev(resDiffNoTonLdAB$Variable)))
pBar + scale_y_continuous(breaks=seq(0, 1, by=0.5))

if (saveplots){
  ggsave(filename=\PtsABcrfDiffSQMsNoTonLdSummary.svg\, width=8, height=3, path=file.path(outFigPath, \svg\))
  unlink(\PtsABcrfDiffSQMsNoTonLdSummary.svg\)
  
  ggsave(filename=\PtsABcrfDiffSQMsNoTonLdSummary.pdf\, width=8, height=3, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABcrfDiffSQMsNoTonLdSummary.pdf\)
}



<!-- rnb-output-end -->

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-->\n\n```r\n```r\n# combine the annoyance perm importance results\n\n# convert each result to a tibble with rownames added to a column, renaming the data column to 'dAnnoy' etc.\nresdAnnoyMnDiffPermImpNoTonLdTblAB <- as.data.frame(resdAnnoyMnPermImpAB$DiffSQMs2/max(resdAnnoyMnPermImpAB$DiffSQMs2)) |>\n  tibble::rownames_to_column(var='Variable')\ncolnames(resdAnnoyMnDiffPermImpNoTonLdTblAB)[2] <- \\dAnnoy\\\n\nresdHiAnnoyDiffPermImpNoTonLdTblAB <- as.data.frame(resdHiAnnoyPermImpAB$DiffSQMs2/max(resdHiAnnoyPermImpAB$DiffSQMs2)) |>\n  tibble::rownames_to_column(var='Variable')\ncolnames(resdHiAnnoyDiffPermImpNoTonLdTblAB)[2] <- \\dHiAnnoy\\\n\n# merge the dataframes\nresDiffPermImpNoTonLdTblAB <- list(resdAnnoyMnDiffPermImpNoTonLdTblAB, resdHiAnnoyDiffPermImpNoTonLdTblAB) |>\n  purrr::reduce(merge, by = c('Variable'), all = T)\n\n# rename the columns\ncolnames(resDiffPermImpNoTonLdTblAB)[2:3] <- c(\\Mean change in annoyance\\, \\%HA | HA' (amb.)\\)\nresDiffPermImpNoTonLdTblAB[is.na(resDiffPermImpNoTonLdTblAB)] <- 0\n\nresDiffNoTonLdAB <- tidyr::pivot_longer(resDiffPermImpNoTonLdTblAB, cols=-Variable, names_to=\\Outcome\\, values_to=\\Imp\\)\n\n# reorder res tibble, descending by the variable Imp grouped sum and create column with new group order as a factor\nresDiffNoTonLdAB <- resDiffNoTonLdAB |> mutate(Variable_sum = sum(Imp), .by=Variable) |> arrange(desc(Variable_sum)) |> group_by(Variable_sum, Variable) |>\n   mutate(Order = cur_group_id()) |> mutate(Order = as.factor(Order)) |> arrange(desc(Order))\n\n# Reorder outcome levels\nresDiffNoTonLdAB$Outcome <- factor(resDiffNoTonLdAB$Outcome, levels=c(\\Mean change in annoyance\\, \\%HA | HA' (amb.)\\))\n\n# plot res as horizontal bar chart, with Imp as y axis, Variable as x axis, Outcome as fill, and Variable_sum as order, relabel x axis with Variable names\npBar <- ggplot(resDiffNoTonLdAB) + geom_col(aes(fill=Outcome, y=Imp, x=Order), colour='grey35', linewidth=0,  width=0.75, show.legend=TRUE) + labs(x=\\Variable\\, y=\\Normalised conditional variable permutation importance\\) + theme(text = element_text(family = \\serif\\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2), legend.position = \\right\\) + coord_flip(ylim=c(-0.1, 1.1)) + scale_fill_manual(values=mycolours) + scale_x_discrete(labels=unique(rev(resDiffNoTonLdAB$Variable)))\npBar + scale_y_continuous(breaks=seq(0, 1, by=0.5))\n\nif (saveplots){\n  ggsave(filename=\\PtsABcrfDiffSQMsNoTonLdSummary.svg\\, width=8, height=3, path=file.path(outFigPath, \\svg\\))\n  unlink(\\PtsABcrfDiffSQMsNoTonLdSummary.svg\\)\n  \n  ggsave(filename=\\PtsABcrfDiffSQMsNoTonLdSummary.pdf\\, width=8, height=3, path=file.path(outFigPath, \\pdf\\))\n  unlink(\\PtsABcrfDiffSQMsNoTonLdSummary.pdf\\)\n}\n\n```\n```\n\n<!-- rnb-source-end -->\n"} -->


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```r
```r
# combine the annoyance perm importance results

# convert each result to a tibble with rownames added to a column, renaming the data column to 'dAnnoy' etc.
resdAnnoyMnDiffPermImpNoTonLdTblAB <- as.data.frame(resdAnnoyMnPermImpAB$DiffSQMs2/max(resdAnnoyMnPermImpAB$DiffSQMs2)) |>
  tibble::rownames_to_column(var='Variable')
colnames(resdAnnoyMnDiffPermImpNoTonLdTblAB)[2] <- \dAnnoy\

resdHiAnnoyDiffPermImpNoTonLdTblAB <- as.data.frame(resdHiAnnoyPermImpAB$DiffSQMs2/max(resdHiAnnoyPermImpAB$DiffSQMs2)) |>
  tibble::rownames_to_column(var='Variable')
colnames(resdHiAnnoyDiffPermImpNoTonLdTblAB)[2] <- \dHiAnnoy\

# merge the dataframes
resDiffPermImpNoTonLdTblAB <- list(resdAnnoyMnDiffPermImpNoTonLdTblAB, resdHiAnnoyDiffPermImpNoTonLdTblAB) |>
  purrr::reduce(merge, by = c('Variable'), all = T)

# rename the columns
colnames(resDiffPermImpNoTonLdTblAB)[2:3] <- c(\Mean change in annoyance\, \%HA | HA' (amb.)\)
resDiffPermImpNoTonLdTblAB[is.na(resDiffPermImpNoTonLdTblAB)] <- 0

resDiffNoTonLdAB <- tidyr::pivot_longer(resDiffPermImpNoTonLdTblAB, cols=-Variable, names_to=\Outcome\, values_to=\Imp\)

# reorder res tibble, descending by the variable Imp grouped sum and create column with new group order as a factor
resDiffNoTonLdAB <- resDiffNoTonLdAB |> mutate(Variable_sum = sum(Imp), .by=Variable) |> arrange(desc(Variable_sum)) |> group_by(Variable_sum, Variable) |>
   mutate(Order = cur_group_id()) |> mutate(Order = as.factor(Order)) |> arrange(desc(Order))

# Reorder outcome levels
resDiffNoTonLdAB$Outcome <- factor(resDiffNoTonLdAB$Outcome, levels=c(\Mean change in annoyance\, \%HA | HA' (amb.)\))

# plot res as horizontal bar chart, with Imp as y axis, Variable as x axis, Outcome as fill, and Variable_sum as order, relabel x axis with Variable names
pBar <- ggplot(resDiffNoTonLdAB) + geom_col(aes(fill=Outcome, y=Imp, x=Order), colour='grey35', linewidth=0,  width=0.75, show.legend=TRUE) + labs(x=\Variable\, y=\Normalised conditional variable permutation importance\) + theme(text = element_text(family = \serif\), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2), legend.position = \right\) + coord_flip(ylim=c(-0.1, 1.1)) + scale_fill_manual(values=mycolours) + scale_x_discrete(labels=unique(rev(resDiffNoTonLdAB$Variable)))
pBar + scale_y_continuous(breaks=seq(0, 1, by=0.5))

if (saveplots){
  ggsave(filename=\PtsABcrfDiffSQMsNoTonLdSummary.svg\, width=8, height=3, path=file.path(outFigPath, \svg\))
  unlink(\PtsABcrfDiffSQMsNoTonLdSummary.svg\)
  
  ggsave(filename=\PtsABcrfDiffSQMsNoTonLdSummary.pdf\, width=8, height=3, path=file.path(outFigPath, \pdf\))
  unlink(\PtsABcrfDiffSQMsNoTonLdSummary.pdf\)
}

```
```

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### Save the results outputs to file


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<!-- rnb-output-begin 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 -->
# Make a list of the summary results
resSummary <- list(resAbsAB, resDiffAB, resAbsNoTonLdAB, resDiffNoTonLdAB)

# Save the results
if (savedata){
  ii <- 0
  temp = list()
  for (res in resSummary){
    ii <- ii + 1
    temp[[ii]] <- data.frame(resSummary[ii])
  }
  openxlsx::write.xlsx(temp, paste(outDataPath, \\\PtsABCRFSummary.xlsx\,
                                   sep=\\),
                       rowNames=TRUE)
}

````

```r
# Make a list of the summary results
resSummary <- list(resAbsAB, resDiffAB, resAbsNoTonLdAB, resDiffNoTonLdAB)

# Save the results
if (savedata){
  ii <- 0
  temp = list()
  for (res in resSummary){
    ii <- ii + 1
    temp[[ii]] <- data.frame(resSummary[ii])
  }
  openxlsx::write.xlsx(temp, paste(outDataPath, \\\PtsABCRFSummary.xlsx\,
                                   sep=\\),
                       rowNames=TRUE)
}

```

---
title: "REFMAP Listening test 1 Parts A & B analysis: Random forest variable importance identification - Revision 1"
output: html_notebook
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

This notebook analyses both parts of the data in terms of variable importance, using a random forest model based on conditional inference trees and a conditional permutation variable importance algorithm.

# Setup

## Load packages

```{r}
# load packages
library(tidyr)
library(ggplot2)
library(party)
library(conflicted)
library(tidyverse)
library(openxlsx)
library(caret)
library(viridis)
library(cowplot)
library(permimp)
# set package parameters
theme_set(theme_bw())

```

```{r}

# plot colour scheme

mycolourlist = list(c(0, 102, 255), c(0, 204, 153), c(255, 0, 102), c(74, 111, 152), c(251, 164, 49), c(204, 153, 255), c(90, 192, 255), c(80, 245, 233), c(255, 90, 192), c(164, 201, 242), c(255, 254, 139), c(255, 243, 255))
mycolours = matrix()

for (ii in 1:length(mycolourlist)){
  mycolours[ii] = rgb(mycolourlist[[ii]][1]/255,
                      mycolourlist[[ii]][2]/255,
                      mycolourlist[[ii]][3]/255)
}

# toggle to save plots
saveplots = FALSE

if (saveplots){
  # set output plot directory
  choose.files(caption="Just cancel this", filters=matrix(data=c(" ", " "), ncol=2))  # workaround for bug in RTerm choose.dir
  outFigPath <- utils::choose.dir(caption="Select output folder to save plots '03 Experiment\\Experiment 1\\Analysis\\Plots'")
  
  if (!dir.exists(file.path(outFigPath, "svg"))){dir.create(file.path(outFigPath, "svg"))}
  if (!dir.exists(file.path(outFigPath, "pdf"))){dir.create(file.path(outFigPath, "pdf"))}
  
}

# toggle to save data
savedata = TRUE

if (savedata){
  # set output plot directory
  if (saveplots==FALSE){
    choose.files(caption="Just cancel this", filters=matrix(data=c(" ", " "), ncol=2))  # workaround for bug in RTerm choose.dir
  }
  outDataPath <- utils::choose.dir(caption="Select output folder to save data '03 Experiment\\Experiment 1\\Analysis\\R'")
}
 

```

# Import data and wrangle

```{r}

stimDatapath <- utils::choose.files(caption=r"(Select refmap_listest1_testdata_ByStim.csv from 03 Experiment\Experiment 1\Analysis\PostProcess)",
                                     filters=matrix(data=c("refmap_listest1_testdata_ByStim.csv", "refmap_listest1_testdata_ByStim.csv"), ncol=2))

stimData <- utils::read.csv(stimDatapath, header=TRUE)

colnames(stimData)[1] <- "Stimulus"

# make response proportions into percentages
stimData[['HighAnnoyPc']] <- stimData[['HighAnnoyProp']]*100
stimData[['dHighAnnoyPc']] <- stimData[['dHighAnnoyProp']]*100

```

```{r}
# function to encode categorical to ordinal numeric variables
encode_ordinal <- function(x, order=unique(x)) {
  x <- as.numeric(factor(x, levels=order, exclude=NULL, order=TRUE))
  x
}

# definition of ordinal variable levels
SNRCats <- c("No UAS", "-16", "-10", "-4", "2", "8")
UASLAeqCats <- c("No UAS", "42", "48", "54", "60")

```

The aggregated data by stimulus are assigned to a dataframe, relevant categorical variables are converted to ordinal, and then the variable subset of interest is selected, NA rows dropped (ie, the 'no UAS' stimuli, as the conditional variable importance algorithm cannot currently handle NA values, which are present in all the UAS dB metrics), and a formula assigned.

```{r}

stimDataNum <- data.frame()

stimDataNum <- cbind(stimData[, 'Stimulus'],
                     stimData[, which(colnames(stimData)=="UASEvents"):
                                which(colnames(stimData)=="UASEventPerMin")],
                    stimData[, which(colnames(stimData)=="UASLAeq"):
                                which(colnames(stimData)=="SNRlevel")],
                    stimData[, which(colnames(stimData)=="IntermitRatioC2MaxLR"):
                                which(colnames(stimData)=="IntermitRatioC5MaxLR")],
                    stimData[, which(colnames(stimData)=="UASLAEMaxLR"):
                                which(colnames(stimData)=="UASEPNLMaxLR")],
                    stimData[, which(colnames(stimData)=="UASLoudECMAPowAvgBin"):
                                which(colnames(stimData)=="UASLoudISO3PowAvgBin")],
                    stimData[, which(colnames(stimData)=="UASTonalECMAAvgMaxLR"):
                                which(colnames(stimData)=="UASSharpvBISO105ExBin")],
                    stimData[, which(colnames(stimData)=="UASImpulsSHMPowAvgMaxLR"):
                                which(colnames(stimData)=="UASPsychAnnoyWillemsen")],
                    stimData[, which(colnames(stimData)=="LAeqLAF90diff"):
                                which(colnames(stimData)=="dPsychAnnoyWillemsen")],
                    stimData[, which(colnames(stimData)=="ValenceMedian"):
                               which(colnames(stimData)=="dHighAnnoyProp")],
                    stimData[, which(colnames(stimData)=="HighAnnoyPc"):
                                which(colnames(stimData)=="dHighAnnoyPc")])

# remove duplicated variables
stimDataNum <- subset(stimDataNum, select = -c(UASLAeq))

colnames(stimDataNum)[1] <- "Stimulus"

# make the discrete ordinal outcome variables factors
stimDataNum[['UASEvents']] <- factor(stimDataNum[['UASEvents']], levels=c(0, 1, 3, 5, 9), order=TRUE)
stimDataNum[['ValenceMedian']] <- factor(stimDataNum[['ValenceMedian']], levels=c(1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5), order=TRUE)
stimDataNum[['ArousalMedian']] <- factor(stimDataNum[['ArousalMedian']], levels=c(1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5), order=TRUE)
stimDataNum[['AnnoyMedian']] <- factor(stimDataNum[['AnnoyMedian']], levels=c(0, 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5,
                                                                                5.5, 6, 6.5, 7, 7.5, 8, 8.5, 9, 9.5, 10), order=TRUE)
stimDataNum[['dValenceMedian']] <- factor(stimDataNum[['dValenceMedian']], levels=c(-4, -3.5, -3, -2.5, -2, -1.5, -1, -0.5, 0,
                                                                                      0.5, 1, 1.5, 2, 2.5, 3, 3.5,  4), order=TRUE)
stimDataNum[['dArousalMedian']] <- factor(stimDataNum[['dArousalMedian']], levels=c(-4, -3.5, -3, -2.5, -2, -1.5, -1, -0.5, 0,
                                                                                      0.5, 1, 1.5, 2, 2.5, 3, 3.5,  4), order=TRUE)
stimDataNum[['dAnnoyMedian']] <- factor(stimDataNum[['dAnnoyMedian']], levels=c(-10, -9.5, -9, -8.5, -8, -7.5, -7, -6.5, -6, -5.5, -5,
                                                                                  -4.5, -4, -3.5, -3, -2.5, -2, -1.5, -1, -0.5,
                                                                                 0, 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5,
                                                                                 5.5, 6, 6.5, 7, 7.5, 8, 8.5, 9, 9.5, 10), order=TRUE)

# omit ambient-only stimuli
stimDataNum <- stimDataNum |> dplyr::filter(UASEvents != 0)


stimDataNum$SNRlevel <- as.numeric(stimDataNum$SNRlevel)
```

# Random forest functions

Write a function to train a conditional-inference random forest (crf) model on input data according to input formula, iterate over input random seeds, average error and variable importance metrics, and output metrics with plotted

## Averaging over multiple random seeds

```{r}

multi_crfReg <- function(dataIn, iVars, dVar, seeds, ntree, mtry, permImpCondThres=0.95, minsplit=20, minbucket=7, nperm=1){
  # initialise variables
  crfOOBErrAll <- 0
  crfOOBRMSE <- 0
  crfOOBMAE <- 0
  crfOOBErrR2 <- 0
  crfMarPermImpVals <- 0
  crfConPermImpVals <- 0
  crfMarPermImpValsPerTree <- data.frame()
  crfConPermImpValsPerTree <- data.frame()
  
  for (iters in 1:length(seeds)){
    
    # formula for regression
    formVars <- reformulate(iVars, dVar)
    
    # set random seed
    set.seed(seeds[iters])
    # train crf model
    crfModel <- party::cforest(formVars, data=dataIn,
                               controls=party::cforest_unbiased(ntree=ntree,
                                                                mtry=mtry,
                                                                minsplit=minsplit,
                                                                minbucket=minbucket))
    
    # get OOB predictions
    crfModelOOB <- predict(crfModel, OOB=TRUE, type='response')
    
    # get OOB error
    crfModelOOBErr <- as.numeric(as.matrix(as.numeric(as.matrix(crfModelOOB))
                                            - as.numeric(as.matrix(crfModel@data@env$response[[names(crfModel@data@env$response)]]))))

    # OOB RMSE, error quartiles and Rsquared
    crfOOBRMSE <- crfOOBRMSE + sqrt(mean(crfModelOOBErr^2))
    crfOOBMAE <- crfOOBMAE + mean(abs(crfModelOOBErr))
    crfOOBErrR2 <- crfOOBErrR2 + cor(as.numeric(as.matrix(crfModelOOB)),
                                     as.numeric(as.matrix(crfModel@data@env$response[[names(crfModel@data@env$response)]])))^2

    # set random seed
    set.seed(seeds[iters])

    # set random seed
    set.seed(seeds[iters])
    # conditional variable permutation importance
    crfConPermImp <- permimp::permimp(crfModel, nperm=nperm, conditional=TRUE, threshold=permImpCondThres, progressBar=FALSE)
    
    crfConPermImpVals <- crfConPermImpVals + crfConPermImp$values
    crfConPermImpValsPerTree <- rbind(crfConPermImpValsPerTree, crfConPermImp$perTree)
  }
  
  # average metrics
  crfOOBErrAll <- crfOOBErrAll/length(seeds)
  crfOOBRMSE <- crfOOBRMSE/length(seeds)
  crfOOBMAE <- crfOOBMAE/length(seeds)
  crfOOBErrR2 <- crfOOBErrR2/length(seeds)
  crfConPermImpVals <- data.frame(CondPermImp=sort(crfConPermImpVals/length(seeds), decreasing=TRUE))
  crfConPermImpValsQtl <- data.frame(apply(crfConPermImpValsPerTree, 2, quantile, probs=c(0.25, 0.50, 0.75)))
  
  resultsOut <- list('OOB_RMSE'=crfOOBRMSE, 'OOB_MAE'=crfOOBMAE, 'Rsquared'=crfOOBErrR2, 'conditional_permimp'=crfConPermImpVals,                      'conditional_permimp_perTree'=crfConPermImpValsPerTree, 'conditional_permimp_qtl'=crfConPermImpValsQtl)
  return(resultsOut)
}

```

## Comparing rankings from two seeds 

```{r}

crfReg <- function(dataIn, iVars, dVar, seeds, ntree, mtry, permImpCondThres=0.95, minsplit=20, minbucket=7, nperm=1){
  # initialise variables
  crfOOBErrAll <- 0
  crfOOBRMSE <- 0
  crfOOBMAE <- 0
  crfOOBErrR2 <- 0
  crfMarPermImpVals <- 0
  crfConPermImpVals <- 0
  crfMarPermImpValsPerTree <- data.frame()
  crfConPermImpValsPerTree <- data.frame()

  # formula for regression
  formVars <- reformulate(iVars, dVar)
  
  for (iters in 1:length(seeds)){
  
    # set random seed
    set.seed(seeds[iters])
    # train crf model
    crfModel <- party::cforest(formVars, data=dataIn,
                               controls=party::cforest_unbiased(ntree=ntree,
                                                                mtry=mtry,
                                                                minsplit=minsplit,
                                                                minbucket=minbucket))
    
    # conditional variable permutation importance
    crfConPermImp <- permimp::permimp(crfModel, nperm=nperm, conditional=TRUE, threshold=permImpCondThres, progressBar=FALSE)
    
    crfConPermImpVals <- crfConPermImp$values
    
    if (iters == 1){
      crfConPermImpVals1 <- data.frame(CondPermImp=sort(crfConPermImpVals, decreasing=TRUE))
      crfConPermImpValsPerTree1 <- crfConPermImp$perTree
      crfConPermImpValsQtl1 <- data.frame(apply(crfConPermImpValsPerTree1, 2, quantile, probs=c(0.25, 0.50, 0.75)))
      
      # get OOB predictions
      crfModelOOB <- predict(crfModel, OOB=TRUE, type='response')
      
      # get OOB error
      crfModelOOBErr <- as.numeric(as.matrix(as.numeric(as.matrix(crfModelOOB))
                                              - as.numeric(as.matrix(crfModel@data@env$response[[names(crfModel@data@env$response)]]))))
      
      # OOB RMSE, error quartiles and Rsquared
      crfOOBRMSE <- sqrt(mean(crfModelOOBErr^2))
      crfOOBMAE <- crfOOBMAE + mean(abs(crfModelOOBErr))
      crfOOBErrR2 <- cor(as.numeric(as.matrix(crfModelOOB)),
                                    as.numeric(as.matrix(crfModel@data@env$response[[names(crfModel@data@env$response)]])))^2

      }
    
    else{
      crfConPermImpValsN <- data.frame(CondPermImp=sort(crfConPermImpVals, decreasing=TRUE))
      
      nVarImpChecks <- min(max(sum(crfConPermImpVals1 >= crfConPermImpVals1$CondPermImp[1]*0.1),
                               sum(crfConPermImpValsN >= crfConPermImpValsN$CondPermImp[1]*0.1)), 4)  # number of variable importance values with a value at least 10% of the highest importance
      if (sum(rownames(crfConPermImpVals1)[1:nVarImpChecks] != rownames(crfConPermImpValsN)[1:nVarImpChecks]) > 0){
        warning("Permutation importance rank order within 10% of max differs between seeds: increase number of trees ('ntree') or number of permutations ('nperm'), or subsample of features ('mtry')")
      }
      else{
        resultsOut <- list('OOB_errors'=crfModelOOBErr, 'OOB_RMSE'=crfOOBRMSE, 'OOB_MAE'=crfOOBMAE, 'Rsquared'=crfOOBErrR2, 'conditional_permimp'=crfConPermImpVals1, 'conditional_permimp_perTree'=crfConPermImpValsPerTree1, 'conditional_permpimp_qtl'=crfConPermImpValsQtl1)
        return(resultsOut)
      }
      
    }
    
  }

}

```

## Hyperparameter tuning

```{r, fig.width=12, fig.height=4}
mtryTune <- function(dataIn, iVars, dVar, seeds, ntrees, minsplit=20, minbucket=7){

  formVars <- reformulate(iVars, dVar)
  
  # set mtry values and corresponding iVars/mtry ratios
  iVars_mtrys <- c(10.5, 5.25, 3.5, 2.75, 2.25, 1.75, 1.5, 1.25)
  mtrys <- as.integer(length(iVars)/iVars_mtrys)
  iVars_mtrys <- iVars_mtrys[mtrys >= 2]  # remove 0 or 1 values
  mtrys <- mtrys[mtrys >= 2]  # remove 0 or 1 values
  
  # remove any duplicated values
  iVars_mtrys <- iVars_mtrys[!(duplicated(mtrys) | duplicated(mtrys, fromLast = TRUE))]
  mtrys <- mtrys[!(duplicated(mtrys) | duplicated(mtrys, fromLast = TRUE))]

  # ensure mtrys is less than length(iVars)
  iVars_mtrys <- iVars_mtrys[mtrys < length(iVars)]
  mtrys <- mtrys[mtrys < length(iVars)]

  resRMSEMap = matrix(data=0, nrow=length(mtrys), ncol=length(ntrees))
  resRsquaredMap = matrix(data=0, nrow=length(mtrys), ncol=length(ntrees))
  resMAEMap = matrix(data=0, nrow=length(mtrys), ncol=length(ntrees))
  
  
  for (ii in seq(1, length(ntrees))){
    
    tuneMod.results <- data.frame(RMSE=numeric(length(mtrys)),
                                Rsquared=numeric(length(mtrys)),
                                MAE=numeric(length(mtrys)))
    
    for (seed in seeds){
      set.seed(seed)
      ntree = ntrees[ii]
      tuneMod <- caret::train(formVars,
                              data=dataIn,
                              method='cforest',
                              controls=party::cforest_unbiased(ntree=ntree,
                                                               minsplit=minsplit,
                                                               minbucket=minbucket),
                              tuneGrid=data.frame(.mtry=mtrys),
                              trControl = trainControl(method = "oob"))
      
      
      
      # accumulate results
      resRMSEMap[, ii] <- resRMSEMap[, ii] + tuneMod$results$RMSE
      resRsquaredMap[, ii] <- resRsquaredMap[, ii] + tuneMod$results$Rsquared
      resMAEMap[, ii] <- resMAEMap[, ii] + tuneMod$results$MAE
      
      tuneMod.results <- tuneMod.results + tuneMod$results[, which(names(tuneMod$results) != 'mtry')]
    }

    # average results
    tuneMod.results <- tuneMod.results/length(seeds)
    tuneMod.results <- cbind(tuneMod.results, data.frame(mtry=mtrys), data.frame(iVars_mtry=iVars_mtrys))

    print(tuneMod.results)

  }
  
  # average results
  resRMSEMap <- resRMSEMap/length(seeds)
  resRsquaredMap <- resRsquaredMap/length(seeds)
  resMAEMap <- resMAEMap/length(seeds)
  
  
  # convert to data frames with mtry as row names and ntree as column names, and convert to long format using tidyverse
  resdfRMSEMap <- as.data.frame(resRMSEMap)
  rownames(resdfRMSEMap) <- mtrys
  colnames(resdfRMSEMap) <- ntrees
  resdfRsquaredMap <- as.data.frame(resRsquaredMap)
  rownames(resdfRsquaredMap) <- mtrys
  colnames(resdfRsquaredMap) <- ntrees
  resdfMAEMap <- as.data.frame(resMAEMap)
  rownames(resdfMAEMap) <- mtrys
  colnames(resdfMAEMap) <- ntrees
  
  
  # convert dataframes to long format using tidyverse
  resdfRMSEMap <- resdfRMSEMap |>
                      rownames_to_column('mtry') |>
                          gather(key='ntree', value='RMSE', -mtry)
  
  resdfRsquaredMap <- resdfRsquaredMap |>
                          rownames_to_column('mtry') |>
                              gather(key='ntree', value='Rsquared', -mtry)
  
  resdfMAEMap <- resdfMAEMap |>
                    rownames_to_column('mtry') |>
                        gather(key='ntree', value='MAE', -mtry)
  
  # ensure ntree and mtry columns are ordered factors
  resdfRMSEMap$ntree <- factor(resdfRMSEMap$ntree, levels=as.character(ntrees))
  resdfRMSEMap$mtry <- factor(resdfRMSEMap$mtry, levels=as.character(mtrys))
  
  resdfRsquaredMap$ntree <- factor(resdfRsquaredMap$ntree, levels=as.character(ntrees))
  resdfRsquaredMap$mtry <- factor(resdfRsquaredMap$mtry, levels=as.character(mtrys))
  
  resdfMAEMap$ntree <- factor(resdfMAEMap$ntree, levels=as.character(ntrees))
  resdfMAEMap$mtry <- factor(resdfMAEMap$mtry, levels=as.character(mtrys))
  
  # plot heatmaps using ggplot, with extreme (min or max) value plotted as overlaid point using annotate and colourbar scale reversed
  pHeatmapRMSE <- ggplot(resdfRMSEMap) +
                    geom_tile(aes(x=ntree, y=mtry, fill=RMSE)) +
                        scale_fill_viridis(option="viridis", direction=-1) +
                          geom_point(data=resdfRMSEMap[which(resdfRMSEMap$RMSE
                                                             == min(resdfRMSEMap$RMSE),
                                                             arr.ind = TRUE),],
                                     aes(x=ntree, y=mtry), colour="red", size=2) +
                            guides(colour = guide_colourbar(reverse=TRUE)) +
                              labs(x="ntree", y="mtry", fill="RMSE") +
                                theme(text = element_text(family = "serif"))
  
  pHeatmapRsquared <- ggplot(resdfRsquaredMap) +
                        geom_tile(aes(x=ntree, y=mtry, fill=Rsquared)) +
                            scale_fill_viridis(option="viridis", direction=1) +
                              geom_point(data=resdfRsquaredMap[which(resdfRsquaredMap$Rsquared
                                                                     == max(resdfRsquaredMap$Rsquared),
                                                                     arr.ind = TRUE),],
                                         aes(x=ntree, y=mtry), colour="red", size=2) +
                                guides(colour = guide_colourbar(reverse=TRUE)) +
                                  labs(x="ntree", y="mtry", fill="Rsquared") +
                                    theme(text = element_text(family = "serif"))
  
  pHeatmapMAE <- ggplot(resdfMAEMap) +
                    geom_tile(aes(x=ntree, y=mtry, fill=MAE)) +
                        scale_fill_viridis(option="viridis", direction=-1) +
                          geom_point(data=resdfMAEMap[which(resdfMAEMap$MAE
                                                            == min(resdfMAEMap$MAE),
                                                            arr.ind = TRUE),],
                                     aes(x=ntree, y=mtry), colour="red", size=2) +
                            guides(colour = guide_colourbar(reverse=TRUE)) +
                              labs(x="ntree", y="mtry", fill="MAE") +
                                theme(text = element_text(family = "serif"))
  
  p <-  cowplot::plot_grid(pHeatmapRMSE,
                           pHeatmapRsquared,
                           pHeatmapMAE,
                           ncol=3, nrow=1)
  
  return(p)
  
}  # end of function

```


# Parts A & B analysis


## Set global parameters

```{r}

permImpCondThres <- 0.95
minsplit <- 20
minbucket <- 7
ntrees <- c(251, 501, 1001, 1501, 2501, 4001, 5501)

eventVar <- "UASEvents"
ambVar <- "AmbientLAeq"

```

## Mean change in annoyance

### Initialise results output variables

```{r}
resdAnnoyMnFitAB <- data.frame(RMSE = numeric(),
                             MAE = numeric(),
                             Rsquared = numeric())
resdAnnoyMnPermImpAB <- list()

```


### All variables (absolute and difference)

#### Remove ambient only stimuli - OMITTED

Here, the 'ambient only' stimuli are removed, as the analysis cannot handle missing values for dB metrics.

```{r}
# stimDataNum <- stimDataNum[complete.cases(stimDataNum),]

```

#### Set variables

```{r}

iVars <- names(stimDataNum)[which(names(stimDataNum) == 'UASEvents'):which(names(stimDataNum) == 'UASImpulsSHM05ExMaxLR')]
iVars <- c(iVars,
           names(stimDataNum)[which(colnames(stimDataNum)=="LAeqLAF90diff"):
                               which(colnames(stimDataNum)=="dImpulsSHM05ExMaxLR")], "SNRlevel")
dVar <- "dAnnoyMean"

seeds <- c(14569, 98651, 54654498, 454948, 41321)

```

#### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
              ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

if (saveplots){
  ggsave(filename="PtsABdAnnoyMnAllVarsHyperTune.svg", width=12, height=4, path=file.path(outFigPath, "svg"))
  unlink("PtsABdAnnoyMnAllVarsHyperTune.svg")

  ggsave(filename="PtsABdAnnoyMnAllVarsHyperTune.pdf", width=12, height=4, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdAnnoyMnAllVarsHyperTune.pdf")
}

```

Selected hyperparameters

```{r}

ntree <- 1501
mtry <- as.integer(length(iVars)/2.25)

```

#### Run model

Train preliminary model

```{r}

nperm <- 5

resultsOutAbsDiffs <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutAbsDiffs$OOB_RMSE
resultsOutAbsDiffs$OOB_MAE
resultsOutAbsDiffs$Rsquared

```

Train multiple seeds model

```{r}

resultsOutAbsDiffs <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutAbsDiffs$OOB_RMSE
resultsOutAbsDiffs$OOB_MAE
resultsOutAbsDiffs$Rsquared
```

```{r}
# store results
resdAnnoyMnFitAB['All vars', 'RMSE'] <- resultsOutAbsDiffs$OOB_RMSE
resdAnnoyMnFitAB['All vars', 'MAE'] <- resultsOutAbsDiffs$OOB_MAE
resdAnnoyMnFitAB['All vars', 'Rsquared'] <- resultsOutAbsDiffs$Rsquared
resdAnnoyMnPermImpAB$AllVars <- resultsOutAbsDiffs$conditional_permimp

```

#### Plot results

```{r, fig.width=8,fig.height=20}
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutAbsDiffs.conimp <- arrange(resultsOutAbsDiffs$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutAbsDiffs.conimp) + geom_col(aes(x=factor(rownames(resultsOutAbsDiffs.conimp), levels=rownames(resultsOutAbsDiffs.conimp)), y=CondPermImp), fill=mycolours[9], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (mean change in annoyance)") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename="PtsABdAnnoyMnAllVarsConPermimp.svg", width=8, height=20, path=file.path(outFigPath, "svg"))
  unlink("PtsABdAnnoyMnAllVarsConPermimp.svg")

  ggsave(filename="PtsABdAnnoyMnAllVarsConPermimp.pdf", width=8, height=20, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdAnnoyMnAllVarsConPermimp.pdf")
}

```

```{r, fig.width=8,fig.height=10}

# Plot only positive values

resultsOutAbsDiffs.conimpPtv <- resultsOutAbsDiffs.conimp |>
                                          rownames_to_column('Metric') |>
                                                filter_if(is.numeric, all_vars(. > 0)) |>
                                                      column_to_rownames('Metric')

pBar <- ggplot(resultsOutAbsDiffs.conimpPtv) + geom_col(aes(x=factor(rownames(resultsOutAbsDiffs.conimpPtv), levels=rownames(resultsOutAbsDiffs.conimpPtv)), y=CondPermImp), fill=mycolours[9], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (mean change in annoyance)") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename="PtsABdAnnoyMnAllVarsConPermimpPtv.svg", width=8, height=10, path=file.path(outFigPath, "svg"))
  unlink("PtsABdAnnoyMnAllVarsConPermimp.svg")
  
  ggsave(filename="PtsABdAnnoyMnAllVarsConPermimpPtv.pdf", width=8, height=10, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdAnnoyMnAllVarsConPermimp.pdf")
}

```
```{r, fig.width=8,fig.height=4}

# Plot only values within 1% of the maximum

resultsOutAbsDiffs.conimp1pc <- resultsOutAbsDiffs.conimp |>
                                          rownames_to_column('Metric') |>
                                                filter_if(is.numeric, all_vars(. > max(resultsOutAbsDiffs.conimp)/100)) |>
                                                      column_to_rownames('Metric')

pBar <- ggplot(resultsOutAbsDiffs.conimp1pc) + geom_col(aes(x=factor(rownames(resultsOutAbsDiffs.conimp1pc), levels=rownames(resultsOutAbsDiffs.conimp1pc)), y=CondPermImp), fill=mycolours[9], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (mean change in annoyance)") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename="PtsABdAnnoyMnAllVarsConPermimp1pc.svg", width=8, height=4, path=file.path(outFigPath, "svg"))
  unlink("PtsABdAnnoyMnAllVarsConPermimp1pc.svg")
  
  ggsave(filename="PtsABdAnnoyMnAllVarsConPermimp1pc.pdf", width=8, height=4, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdAnnoyMnAllVarsConPermimp1pc.pdf")
}

```


### Absolute variables

#### Set variables

```{r}

iVars <- names(stimDataNum)[which(names(stimDataNum) == 'UASEvents'):which(names(stimDataNum) == 'UASImpulsSHM05ExMaxLR')]
iVars <- iVars[! iVars %in% c('SNRlevel', 'IntermitRatioC2MaxLR', 'IntermitRatioC3MaxLR', 'IntermitRatioC5MaxLR')]

dVar <- "dAnnoyMean"

seeds <- c(578312, 544, 84894, 54654, 153157)

```

#### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
              ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

if (saveplots){
  ggsave(filename="PtsABdAnnoyMnAbsVarsHyperTune.svg", width=12, height=4, path=file.path(outFigPath, "svg"))
  unlink("PtsABdAnnoyMnAbsVarsHyperTune.svg")

  ggsave(filename="PtsABdAnnoyMnAbsVarsHyperTune.pdf", width=12, height=4, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdAnnoyMnAbsVarsHyperTune.pdf")
}

```

Selected hyperparameters

```{r}

ntree <- 2501
mtry <- as.integer(length(iVars)/1.25)

```

#### Run model

Train preliminary model

```{r}

nperm <- 15

resultsOutAbs <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutAbs$OOB_RMSE
resultsOutAbs$OOB_MAE
resultsOutAbs$Rsquared

```
Train multiple seeds model

```{r}

resultsOutAbs <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutAbs$OOB_RMSE
resultsOutAbs$OOB_MAE
resultsOutAbs$Rsquared

```


```{r}

# store results
resdAnnoyMnFitAB['Abs vars', 'RMSE'] <- resultsOutAbs$OOB_RMSE
resdAnnoyMnFitAB['Abs vars', 'MAE'] <- resultsOutAbs$OOB_MAE
resdAnnoyMnFitAB['Abs vars', 'Rsquared'] <- resultsOutAbs$Rsquared
resdAnnoyMnPermImpAB$AbsVars <- resultsOutAbs$conditional_permimp

```

#### Plot results

```{r, fig.width=8,fig.height=11.5}
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutAbs.conimp <- arrange(resultsOutAbs$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutAbs.conimp) + geom_col(aes(x=factor(rownames(resultsOutAbs.conimp), levels=rownames(resultsOutAbs.conimp)), y=CondPermImp), fill=mycolours[1], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (mean change in annoyance)") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) +
  coord_flip()
pBar

if (saveplots){
  ggsave(filename="PtsABdAnnoyMnAbsVarsConPermimp.svg", width=8, height=11.5, path=file.path(outFigPath, "svg"))
  unlink("PtsABdAnnoyMnAbsVarsConPermimp.svg")
  
  ggsave(filename="PtsABdAnnoyMnAbsVarsConPermimp.pdf", width=8, height=11.5, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdAnnoyMnAbsVarsConPermimp.pdf")
}
```


```{r, fig.width=8,fig.height=8}

# Plot only positive values

resultsOutAbs.conimpPtv <- resultsOutAbs.conimp |>
                                          rownames_to_column('Metric') |>
                                                filter_if(is.numeric, all_vars(. > 0)) |>
                                                      column_to_rownames('Metric')

pBar <- ggplot(resultsOutAbs.conimpPtv) + geom_col(aes(x=factor(rownames(resultsOutAbs.conimpPtv), levels=rownames(resultsOutAbs.conimpPtv)), y=CondPermImp), fill=mycolours[1], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (mean change in annoyance)") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename="PtsABdAnnoyMnAbsVarsConPermimpPtv.svg", width=8, height=8, path=file.path(outFigPath, "svg"))
  unlink("PtsABdAnnoyMnAbsVarsConPermimpPtv.svg")
  
  ggsave(filename="PtsABdAnnoyMnAbsVarsConPermimpPtv.pdf", width=8, height=8, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdAnnoyMnAbsVarsConPermimpPtv.pdf")
}

```

```{r, fig.width=8,fig.height=2.6}

# Plot only values within 1% of the maximum

resultsOutAbs.conimp1pc <- resultsOutAbs.conimp |>
                                          rownames_to_column('Metric') |>
                                                filter_if(is.numeric, all_vars(. > max(resultsOutAbs.conimp)/100)) |>
                                                      column_to_rownames('Metric')

pBar <- ggplot(resultsOutAbs.conimp1pc) + geom_col(aes(x=factor(rownames(resultsOutAbs.conimp1pc), levels=rownames(resultsOutAbs.conimp1pc)), y=CondPermImp), fill=mycolours[1], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (mean change in annoyance)") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename="PtsABdAnnoyMnAbsVarsConPermimp1pc.svg", width=8, height=2.6, path=file.path(outFigPath, "svg"))
  unlink("PtsABdAnnoyMnAbsVarsConPermimp1pc.svg")
  
  ggsave(filename="PtsABdAnnoyMnAbsVarsConPermimp1pc.pdf", width=8, height=2.6, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdAnnoyMnAbsVarsConPermimp1pc.pdf")
}

```

Selected metric

```{r}

absVar <- "UASLAEMaxLR"

```


### SQM analysis

#### Replace ambient only stimuli

The ambient-only stimuli are NOT replaced here, as the analysis cannot handle missing values for dB metrics.

#### Individual SQMs

##### Sharpness

###### Set variables

```{r}

iVars <- c(absVar, eventVar, ambVar, "UASSharpAurISO3PowAvgMaxLR", "UASSharpAurISO305ExMaxLR", "UASSharpAurSHMPowAvgMaxLR", "UASSharpAurSHM05ExMaxLR", "UASSharpAurISO1PowAvgMaxLR", "UASSharpAurISO105ExMaxLR", "UASSharpvBISO1PowAvgMaxLR", "UASSharpvBISO105ExMaxLR", "UASSharpDINPowAvgMaxLR", "UASSharpDIN05ExMaxLR")
dVar <- "dAnnoyMean"

seeds <- c(7041, 905, 4984651, 6513213, 120651)

```

###### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```

Selected hyperparameters

```{r}

ntree <- 251
mtry <- as.integer(length(iVars)/1.25)

```

###### Run model

Train preliminary model

```{r}

nperm <- 5

resultsOutSharp <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSharp$OOB_RMSE
resultsOutSharp$OOB_MAE
resultsOutSharp$Rsquared

```

Train multiple seeds model

```{r}

resultsOutSharp <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSharp$OOB_RMSE
resultsOutSharp$OOB_MAE
resultsOutSharp$Rsquared

```


```{r}
# store results
resdAnnoyMnFitAB['Abs sharp', 'RMSE'] <- resultsOutSharp$OOB_RMSE
resdAnnoyMnFitAB['Abs sharp', 'MAE'] <- resultsOutSharp$OOB_MAE
resdAnnoyMnFitAB['Abs sharp', 'Rsquared'] <- resultsOutSharp$Rsquared
resdAnnoyMnPermImpAB$AbsSharp <- resultsOutSharp$conditional_permimp

```

###### Plot results

```{r, fig.width=8,fig.height=4.9}
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutSharp.conimp <- arrange(resultsOutSharp$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutSharp.conimp) + geom_col(aes(x=factor(rownames(resultsOutSharp.conimp), levels=rownames(resultsOutSharp.conimp)), y=CondPermImp), fill=mycolours[2], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (mean change in annoyance)") + ggtitle("Sharpness") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename="PtsABdAnnoyMnSharpConPermimp.svg", width=8, height=4.9, path=file.path(outFigPath, "svg"))
  unlink("PtsABdAnnoyMnSharpConPermimp.svg")
  
  ggsave(filename="PtsABdAnnoyMnSharpConPermimp.pdf", width=8, height=4.9, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdAnnoyMnSharpConPermimp.pdf")
}


```

Selected metric

```{r}

sharpVar <- "UASSharpAurISO305ExMaxLR"

```

##### Tonal loudness and tonality

###### Set variables

```{r}

iVars <- c(absVar, eventVar, ambVar, "UASTonalECMAAvgMaxLR", "UASTonalSHMInt05ExMaxLR", "UASTonalSHMIntAvgMaxLR", "UASTonalECMA05ExMaxLR", "UASTonLdECMAPowAvgBin", "UASTonLdECMA05ExBin", "UASTonalAurAvgMaxLR", "UASTonalAur05ExMaxLR", "UASTonalAur10ExMaxLR")
dVar <- "dAnnoyMean"

seeds <- c(540, 104798, 456464, 87331, 94564)

```

###### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```

Selected hyperparameters

```{r}

ntree <- 501
mtry <- as.integer(length(iVars)/1.25)

```

###### Run model

Train preliminary model

```{r}
# Tonality with tonal loudness

nperm <- 5

resultsOutTonal1 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutTonal1$OOB_RMSE
resultsOutTonal1$OOB_MAE
resultsOutTonal1$Rsquared
```

Train multiple seeds model

```{r}
# Tonality with tonal loudness

resultsOutTonal1 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutTonal1$OOB_RMSE
resultsOutTonal1$OOB_MAE
resultsOutTonal1$Rsquared

```

```{r}
# store results
resdAnnoyMnFitAB['Abs tonal inc loud', 'RMSE'] <- resultsOutTonal1$OOB_RMSE
resdAnnoyMnFitAB['Abs tonal inc loud', 'MAE'] <- resultsOutTonal1$OOB_MAE
resdAnnoyMnFitAB['Abs tonal inc loud', 'Rsquared'] <- resultsOutTonal1$Rsquared
resdAnnoyMnPermImpAB$AbsTonal1 <- resultsOutTonal1$conditional_permimp

```

###### Plot results

```{r, fig.width=8,fig.height=3.8}

par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutTonal1.conimp <- arrange(resultsOutTonal1$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutTonal1.conimp) + geom_col(aes(x=factor(rownames(resultsOutTonal1.conimp), levels=rownames(resultsOutTonal1.conimp)), y=CondPermImp), fill=mycolours[3], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (mean change in annoyance)") + ggtitle("Tonality inc. tonal loudness") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 1.4))
pBar

if (saveplots){
  ggsave(filename="PtsABdAnnoyMnTonalLdConPermimp.svg", width=8, height=3.8, path=file.path(outFigPath, "svg"))
  unlink("PtsABdAnnoyMnTonalLdConPermimp.svg")
  
  ggsave(filename="PtsABdAnnoyMnTonalLdConPermimp.pdf", width=8, height=3.8, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdAnnoyMnTonalLdConPermimp.pdf")
}

```

Selected metric

```{r}

tonLdVar <- "UASTonLdECMAPowAvgBin"

```

##### Tonality without tonal loudness

###### Set variables

```{r}

iVars <- c(absVar, eventVar, ambVar, "UASTonalECMAAvgMaxLR", "UASTonalSHMInt05ExMaxLR", "UASTonalSHMIntAvgMaxLR", "UASTonalECMA05ExMaxLR", "UASTonalAurAvgMaxLR", "UASTonalAur05ExMaxLR", "UASTonalAur10ExMaxLR")
dVar <- "dAnnoyMean"

seeds <- c(156089, 5860, 10528, 89541, 4685146)

```

###### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```

Selected hyperparameters

```{r}

ntree <- 1001
mtry <- as.integer(length(iVars)/1.25)

```

###### Run model

Train preliminary model

```{r}
# Tonality

nperm <- 5

resultsOutTonal2 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutTonal2$OOB_RMSE
resultsOutTonal2$OOB_MAE
resultsOutTonal2$Rsquared

```

Train multiple seeds model

```{r}
# Tonality

resultsOutTonal2 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutTonal2$OOB_RMSE
resultsOutTonal2$OOB_MAE
resultsOutTonal2$Rsquared

```


```{r}

# store results
resdAnnoyMnFitAB['Abs tonal no loud', 'RMSE'] <- resultsOutTonal2$OOB_RMSE
resdAnnoyMnFitAB['Abs tonal no loud', 'MAE'] <- resultsOutTonal2$OOB_MAE
resdAnnoyMnFitAB['Abs tonal no loud', 'Rsquared'] <- resultsOutTonal2$Rsquared
resdAnnoyMnPermImpAB$AbsTonal2 <- resultsOutTonal2$conditional_permimp

```

###### Plot results

```{r, fig.width=8,fig.height=3.2}
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutTonal2.conimp <- arrange(resultsOutTonal2$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutTonal2.conimp) + geom_col(aes(x=factor(rownames(resultsOutTonal2.conimp), levels=rownames(resultsOutTonal2.conimp)), y=CondPermImp), fill=mycolours[3], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (mean change in annoyance)") + ggtitle("Tonality w/o tonal loudness") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 1.4))
pBar

if (saveplots){
  ggsave(filename="PtsABdAnnoyMnTonalConPermimp.svg", width=8, height=3.2, path=file.path(outFigPath, "svg"))
  unlink("PtsABdAnnoyMnTonalConPermimp.svg")
  
  ggsave(filename="PtsABdAnnoyMnTonalConPermimp.pdf", width=8, height=3.2, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdAnnoyMnTonalConPermimp.pdf")
}


```

Selected metric

```{r}

tonalVar <- "UASTonalSHMInt05ExMaxLR"

```

##### Fluctuation strength

###### Set variables

```{r}

# Fluctuation strength
iVars <- c(absVar, eventVar, ambVar, "UASFluctSHM10ExBin", "UASFluctSHM05ExBin", "UASFluctFZ10ExMaxLR", "UASFluctFZ05ExMaxLR", "UASFluctOV10ExMaxLR", "UASFluctOV05ExMaxLR")
dVar <- "dAnnoyMean"

seeds <- c(25107, 546098, 195, 5937, 102658)

```


###### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```

Selected hyperparameters

```{r}

ntree <- 501
mtry <- as.integer(length(iVars)/1.25)

```

###### Run model

Train preliminary model

```{r}

nperm <- 5

resultsOutFluct <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutFluct$OOB_RMSE
resultsOutFluct$OOB_MAE
resultsOutFluct$Rsquared

```

Train multiple seeds model

```{r}

resultsOutFluct <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutFluct$OOB_RMSE
resultsOutFluct$OOB_MAE
resultsOutFluct$Rsquared

```


```{r}

# store results
resdAnnoyMnFitAB['Abs fluct', 'RMSE'] <- resultsOutFluct$OOB_RMSE
resdAnnoyMnFitAB['Abs fluct', 'MAE'] <- resultsOutFluct$OOB_MAE
resdAnnoyMnFitAB['Abs fluct', 'Rsquared'] <- resultsOutFluct$Rsquared
resdAnnoyMnPermImpAB$AbsFluct <- resultsOutFluct$conditional_permimp

```

###### Plot results

```{r, fig.width=8,fig.height=2.9}
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutFluct.conimp <- arrange(resultsOutFluct$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutFluct.conimp) + geom_col(aes(x=factor(rownames(resultsOutFluct.conimp), levels=rownames(resultsOutFluct.conimp)), y=CondPermImp), fill=mycolours[4], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (mean change in annoyance)") + ggtitle("Fluctuation strength") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename="PtsABdAnnoyMnFluctConPermimp.svg", width=8, height=2.9, path=file.path(outFigPath, "svg"))
  unlink("PtsABdAnnoyMnFluctConPermimp.svg")
  
  ggsave(filename="PtsABdAnnoyMnFluctConPermimp.pdf", width=8, height=2.9, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdAnnoyMnFluctConPermimp.pdf")
}

```

Selected metric

```{r}

fluctVar <- "UASFluctOV10ExMaxLR"

```

##### Roughness

###### Set variables

```{r}

# Roughness
iVars <- c(absVar, eventVar, ambVar, "UASRoughECMA10ExBin", "UASRoughECMA05ExBin", "UASRoughFZ10ExMaxLR", "UASRoughFZ05ExMaxLR", "UASRoughDW10ExMaxLR", "UASRoughDW05ExMaxLR")
dVar <- "dAnnoyMean"

seeds <- c(4701, 52187, 16589, 65217, 16893)

```

###### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```

Selected hyperparameters

```{r}

ntree <- 1001
mtry <- as.integer(length(iVars)/1.25)

```

###### Run model

Train preliminary model

```{r}

nperm <- 5

resultsOutRough <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutRough$OOB_RMSE
resultsOutRough$OOB_MAE
resultsOutRough$Rsquared

```

Train multiple seeds model

```{r}

resultsOutRough <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutRough$OOB_RMSE
resultsOutRough$OOB_MAE
resultsOutRough$Rsquared

```

```{r}
# store results
resdAnnoyMnFitAB['Abs rough', 'RMSE'] <- resultsOutRough$OOB_RMSE
resdAnnoyMnFitAB['Abs rough', 'MAE'] <- resultsOutRough$OOB_MAE
resdAnnoyMnFitAB['Abs rough', 'Rsquared'] <- resultsOutRough$Rsquared
resdAnnoyMnPermImpAB$AbsRough <- resultsOutRough$conditional_permimp

```

###### Plot results

```{r, fig.width=8,fig.height=2.9}
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutRough.conimp <- arrange(resultsOutRough$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutRough.conimp) + geom_col(aes(x=factor(rownames(resultsOutRough.conimp), levels=rownames(resultsOutRough.conimp)), y=CondPermImp), fill=mycolours[5], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (mean change in annoyance)") + ggtitle("Roughness") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename="PtsABdAnnoyMnRoughConPermimp.svg", width=8, height=2.9, path=file.path(outFigPath, "svg"))
  unlink("PtsABdAnnoyMnRoughConPermimp.svg")
  
  ggsave(filename="PtsABdAnnoyMnRoughConPermimp.pdf", width=8, height=2.9, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdAnnoyMnRoughConPermimp.pdf")
}


```

Selected metric

```{r}

roughVar <- "UASRoughFZ05ExMaxLR"

```

##### Impulsiveness

###### Set variables

```{r}
# Impulsiveness
iVars <- c(absVar, eventVar, ambVar, "UASImpulsSHMAvgMaxLR", "UASImpulsSHM05ExMaxLR", "UASImpulsSHMPowAvgMaxLR")
dVar <- "dAnnoyMean"

seeds <- c(8495, 59867, 5416, 9843, 86)

```

###### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```

Selected hyperparameters

```{r}

ntree <- 251
mtry <- as.integer(length(iVars)/1.75)

```


###### Run model

Train preliminary model

```{r}

nperm <- 5

resultsOutImpuls <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutImpuls$OOB_RMSE
resultsOutImpuls$OOB_MAE
resultsOutImpuls$Rsquared

```

Train multiple seeds model

```{r}

resultsOutImpuls <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutImpuls$OOB_RMSE
resultsOutImpuls$OOB_MAE
resultsOutImpuls$Rsquared

```

```{r}

# store results
resdAnnoyMnFitAB['Abs impuls', 'RMSE'] <- resultsOutImpuls$OOB_RMSE
resdAnnoyMnFitAB['Abs impuls', 'MAE'] <- resultsOutImpuls$OOB_MAE
resdAnnoyMnFitAB['Abs impuls', 'Rsquared'] <- resultsOutImpuls$Rsquared
resdAnnoyMnPermImpAB$AbsImpuls <- resultsOutImpuls$conditional_permimp

```

###### Plot results

```{r, fig.width=8,fig.height=2}
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutImpuls.conimp <- arrange(resultsOutImpuls$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutImpuls.conimp) + geom_col(aes(x=factor(rownames(resultsOutImpuls.conimp), levels=rownames(resultsOutImpuls.conimp)), y=CondPermImp), fill=mycolours[6], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (mean change in annoyance)") + ggtitle("Impulsiveness") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename="PtsABdAnnoyMnImpulsConPermimp.svg", width=8, height=2, path=file.path(outFigPath, "svg"))
  unlink("PtsABdAnnoyMnImpulsConPermimp.svg")
  
  ggsave(filename="PtsABdAnnoyMnImpulsConPermimp.pdf", width=8, height=2, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdAnnoyMnImpulsConPermimp.pdf")
}

```

Selected metric

```{r}

impulsVar <- "UASImpulsSHMAvgMaxLR"

```

#### SQM and loudness comparison

Now the highest importance SQMs are ranked against each other, controlling for UAS loudness and ambient LAeq.

##### Include tonal loudness

###### Set variables

```{r}

iVars <- c(absVar, eventVar, ambVar, sharpVar, tonLdVar, fluctVar, roughVar, impulsVar)
dVar <- "dAnnoyMean"

seeds <- c(70498, 4, 14986, 453, 864)

```

###### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```

Selected hyperparameters

```{r}

ntree <- 4001
mtry <- as.integer(length(iVars)/1.25)

```

###### Run model

Train preliminary model

```{r}

nperm <- 5

resultsOutSQMs1 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSQMs1$OOB_RMSE
resultsOutSQMs1$OOB_MAE
resultsOutSQMs1$Rsquared

```

Train multiple seeds model

```{r}

resultsOutSQMs1 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSQMs1$OOB_RMSE
resultsOutSQMs1$OOB_MAE
resultsOutSQMs1$Rsquared

```

```{r}

# store results
resdAnnoyMnFitAB['Abs SQMs inc tonal loud', 'RMSE'] <- resultsOutSQMs1$OOB_RMSE
resdAnnoyMnFitAB['Abs SQMs inc tonal loud', 'MAE'] <- resultsOutSQMs1$OOB_MAE
resdAnnoyMnFitAB['Abs SQMs inc tonal loud', 'Rsquared'] <- resultsOutSQMs1$Rsquared
resdAnnoyMnPermImpAB$AbsSQMs1 <- resultsOutSQMs1$conditional_permimp

```

###### Plot results

```{r, fig.width=8,fig.height=2.4}
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutSQMs1.conimp <- arrange(resultsOutSQMs1$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutSQMs1.conimp) + geom_col(aes(x=factor(rownames(resultsOutSQMs1.conimp), levels=rownames(resultsOutSQMs1.conimp)), y=CondPermImp), fill=mycolours[7], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (mean change in annoyance)") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 1.3))
pBar

if (saveplots){
  ggsave(filename="PtsABdAnnoyMnAbsSQMsTonLdConPermimp.svg", width=8, height=2.4, path=file.path(outFigPath, "svg"))
  unlink("PtsABdAnnoyMnAbsSQMsTonLdConPermimp.svg")
  
  ggsave(filename="PtsABdAnnoyMnAbsSQMsTonLdConPermimp.pdf", width=8, height=2.4, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdAnnoyMnAbsSQMsTonLdConPermimp.pdf")
}

```

##### Exclude tonal loudness

###### Set variables

```{r}

iVars <- c(absVar, eventVar, ambVar, sharpVar, tonalVar, fluctVar, roughVar, impulsVar)
dVar <- "dAnnoyMean"

seeds <- c(546, 57203, 270835, 60592, 8094)

```

###### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```

Selected hyperparameters

```{r}

ntree <- 501
mtry <- as.integer(length(iVars)/1.5)

```

###### Run model

Train preliminary model

```{r}

nperm <- 5

resultsOutSQMs2 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSQMs2$OOB_RMSE
resultsOutSQMs2$OOB_MAE
resultsOutSQMs2$Rsquared

```

Train multiple seeds model

```{r}

resultsOutSQMs2 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSQMs2$OOB_RMSE
resultsOutSQMs2$OOB_MAE
resultsOutSQMs2$Rsquared

```

```{r}

# store results
resdAnnoyMnFitAB['Abs SQMs no tonal loud', 'RMSE'] <- resultsOutSQMs2$OOB_RMSE
resdAnnoyMnFitAB['Abs SQMs no tonal loud', 'MAE'] <- resultsOutSQMs2$OOB_MAE
resdAnnoyMnFitAB['Abs SQMs no tonal loud', 'Rsquared'] <- resultsOutSQMs2$Rsquared
resdAnnoyMnPermImpAB$AbsSQMs2 <- resultsOutSQMs2$conditional_permimp

```

###### Plot results

```{r, fig.width=8,fig.height=2.4}
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutSQMs2.conimp <- arrange(resultsOutSQMs2$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutSQMs2.conimp) + geom_col(aes(x=factor(rownames(resultsOutSQMs2.conimp), levels=rownames(resultsOutSQMs2.conimp)), y=CondPermImp), fill=mycolours[7], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (mean change in annoyance)") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 1.3))
pBar

if (saveplots){
  ggsave(filename="PtsABdAnnoyMnAbsSQMsNoTonLdConPermimp.svg", width=8, height=2.4, path=file.path(outFigPath, "svg"))
  unlink("PtsABdAnnoyMnAbsSQMsNoTonLdConPermimp.svg")
  
  ggsave(filename="PtsABdAnnoyMnAbsSQMsNoTonLdConPermimp.pdf", width=8, height=2.4, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdAnnoyMnAbsSQMsNoTonLdConPermimp.pdf")
}

```

### Difference variables

Next, the difference metrics are analysed, 

#### Remove ambient only stimuli - OMITTED

Here, the 'ambient only' stimuli are removed, as the analysis cannot handle missing values for dB metrics.

```{r}
# stimDataNum <- stimDataNum[complete.cases(stimDataNum),]
# stimDataNum$UASLAeq <- as.numeric(stimDataNum$UASLAeq)
# stimDataNum$SNRlevel <- as.numeric(stimDataNum$SNRlevel)
```

#### Set variables

```{r}

iVars <- c(names(stimDataNum)[which(colnames(stimDataNum)=="LAeqLAF90diff"):
                               which(colnames(stimDataNum)=="dImpulsSHM05ExMaxLR")],
           "UASEvents", "SNRlevel", "IntermitRatioC2MaxLR", "IntermitRatioC3MaxLR", "IntermitRatioC5MaxLR")
dVar <- "dAnnoyMean"

seeds <- c(568392, 498, 4089, 78132, 741809)

```

#### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```

Selected hyperparameters

```{r}

ntree <- 501
mtry <- as.integer(length(iVars)/1.5)

```

#### Run model

Train preliminary model

```{r}

nperm <- 10

resultsOutDiffs <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutDiffs$OOB_RMSE
resultsOutDiffs$OOB_MAE
resultsOutDiffs$Rsquared

```

Train multiple seeds model

```{r}

resultsOutDiffs <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutDiffs$OOB_RMSE
resultsOutDiffs$OOB_MAE
resultsOutDiffs$Rsquared

```

```{r}

# store results
resdAnnoyMnFitAB['Diff vars', 'RMSE'] <- resultsOutDiffs$OOB_RMSE
resdAnnoyMnFitAB['Diff vars', 'MAE'] <- resultsOutDiffs$OOB_MAE
resdAnnoyMnFitAB['Diff vars', 'Rsquared'] <- resultsOutDiffs$Rsquared
resdAnnoyMnPermImpAB$DiffVars <- resultsOutDiffs$conditional_permimp

```

#### Plot results

```{r, fig.width=8,fig.height=10}
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutDiffs.conimp <- arrange(resultsOutDiffs$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutDiffs.conimp) + geom_col(aes(x=factor(rownames(resultsOutDiffs.conimp), levels=rownames(resultsOutDiffs.conimp)), y=CondPermImp), fill=mycolours[8], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (mean change in annoyance)") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename="PtsABdAnnoyMnDiffVarsConPermimp.svg", width=8, height=10, path=file.path(outFigPath, "svg"))
  unlink("PtsABdAnnoyMnDiffVarsConPermimp.svg")

  ggsave(filename="PtsABdAnnoyMnDiffVarsConPermimp.pdf", width=8, height=10, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdAnnoyMnDiffVarsConPermimp.pdf")
}

```

```{r, fig.width=8,fig.height=8}

# Plot only positive values
resultsOutDiffs.conimpPtv <- resultsOutDiffs.conimp |>
                                        rownames_to_column('Metric') |>
                                            filter_if(is.numeric, all_vars(. > 0)) |>
                                                  column_to_rownames('Metric')

pBar <- ggplot(resultsOutDiffs.conimpPtv) + geom_col(aes(x=factor(rownames(resultsOutDiffs.conimpPtv), levels=rownames(resultsOutDiffs.conimpPtv)), y=CondPermImp), fill=mycolours[8], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (mean change in annoyance)") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename="PtsABdAnnoyMnDiffVarsConPermimpPtv.svg", width=8, height=8, path=file.path(outFigPath, "svg"))
  unlink("PtsABdAnnoyMnDiffVarsConPermimpPtv.svg")

  ggsave(filename="PtsABdAnnoyMnDiffVarsConPermimpPtv.pdf", width=8, height=8, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdAnnoyMnDiffVarsConPermimpPtv.pdf")
}

```
```{r, fig.width=8,fig.height=4}

# Plot only values within 1% of the maximum
resultsOutDiffs.conimp1pc <- resultsOutDiffs.conimp |>
                                        rownames_to_column('Metric') |>
                                            filter_if(is.numeric, all_vars(. > max(resultsOutDiffs.conimp)/100)) |>
                                                  column_to_rownames('Metric')

pBar <- ggplot(resultsOutDiffs.conimp1pc) + geom_col(aes(x=factor(rownames(resultsOutDiffs.conimp1pc), levels=rownames(resultsOutDiffs.conimp1pc)), y=CondPermImp), fill=mycolours[8], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (mean change in annoyance)") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename="PtsABdAnnoyMnDiffVarsConPermimp1pc.svg", width=8, height=4, path=file.path(outFigPath, "svg"))
  unlink("PtsABdAnnoyMnDiffVarsConPermimp1pc.svg")

  ggsave(filename="PtsABdAnnoyMnDiffVarsConPermimp1pc.pdf", width=8, height=4, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdAnnoyMnDiffVarsConPermimp1pc.pdf")
}

```

```{r, fig.width=4,fig.height=4}

# Plot only values within 1% of the maximum
resultsOutDiffs.conimp1pc <- resultsOutDiffs.conimp |>
                                        rownames_to_column('Metric') |>
                                            filter_if(is.numeric, all_vars(. > max(resultsOutDiffs.conimp)/100)) |>
                                                  column_to_rownames('Metric')

pBar <- ggplot(resultsOutDiffs.conimp1pc) + geom_col(aes(x=factor(rownames(resultsOutDiffs.conimp1pc), levels=rownames(resultsOutDiffs.conimp1pc)), y=CondPermImp), fill=mycolours[8], width=0.5) + labs(x="Variable", y="Conditional variable permutation\nimportance (mean change in annoyance)") + theme(text = element_text(family = "serif"), axis.title.x=element_text(family = "serif", size=9), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

filename = "PtsABdAnnoyMnDiffVarsConPermimp1pcNw"

if (saveplots){
  ggsave(filename=paste(filename, ".svg"), width=4, height=4, path=file.path(outFigPath, "svg"))
  unlink(paste(filename, ".svg"))

  ggsave(filename=paste(filename, ".pdf"), width=4, height=4, path=file.path(outFigPath, "pdf"))
  unlink(paste(filename, ".svg"))
}

```


Selected metric

```{r}

diffVar <- "LAELAF50diff"

```

### dSQM analysis

The ambient-only stimuli are NOT replaced here, as the difference analysis includes dB metrics.

#### Individual SQMs

##### dSharpness

###### Set variables

```{r}

iVars <- c(diffVar, eventVar, "dSharpAurISO3PowAvgMaxLR", "dSharpAurISO305ExMaxLR", "dSharpAurSHMPowAvgMaxLR", "dSharpAurSHM05ExMaxLR")
dVar <- "dAnnoyMean"

seeds <- c(84194, 905, 64815, 928054, 625091)

```

###### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```

Selected hyperparameters

```{r}

ntree <-251
mtry <- as.integer(length(iVars)/1.75)

```

###### Run model

Train preliminary model

```{r}

nperm <- 5

resultsOutSharp <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSharp$OOB_RMSE
resultsOutSharp$OOB_MAE
resultsOutSharp$Rsquared

```

Train multiple seeds model

```{r}

resultsOutSharp <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSharp$OOB_RMSE
resultsOutSharp$OOB_MAE
resultsOutSharp$Rsquared

```


```{r}
# store results
resdAnnoyMnFitAB['Diff sharp', 'RMSE'] <- resultsOutSharp$OOB_RMSE
resdAnnoyMnFitAB['Diff sharp', 'MAE'] <- resultsOutSharp$OOB_MAE
resdAnnoyMnFitAB['Diff sharp', 'Rsquared'] <- resultsOutSharp$Rsquared
resdAnnoyMnPermImpAB$DiffSharp <- resultsOutSharp$conditional_permimp

```

###### Plot results

```{r, fig.width=8,fig.height=2.6}
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutSharp.conimp <- arrange(resultsOutSharp$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutSharp.conimp) + geom_col(aes(x=factor(rownames(resultsOutSharp.conimp), levels=rownames(resultsOutSharp.conimp)), y=CondPermImp), fill=mycolours[2], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (mean change in annoyance)") + ggtitle("dSharpness") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename="PtsABdAnnoyMndSharpConPermimp.svg", width=8, height=2.6, path=file.path(outFigPath, "svg"))
  unlink("PtsABdAnnoyMndSharpConPermimp.svg")
  
  ggsave(filename="PtsABdAnnoyMndSharpConPermimp.pdf", width=8, height=2.6, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdAnnoyMndSharpConPermimp.pdf")
}


```

Selected metric

```{r}

dSharpVar <- "dSharpAurISO3PowAvgMaxLR"

```

##### dTonal loudness and dtonality

###### Set variables

```{r}

iVars <- c(diffVar, eventVar, "dTonalECMAAvgMaxLR", "dTonalSHMInt05ExMaxLR", "dTonalSHMIntAvgMaxLR", "dTonalECMA05ExMaxLR", "dTonLdECMAPowAvgBin", "dTonLdECMA05ExBin")
dVar <- "dAnnoyMean"

seeds <- c(561684, 104798, 1536, 48, 48561)

```

###### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```

Selected hyperparameters

```{r}

ntree <- 1501
mtry <- as.integer(length(iVars)/1.25)

```

###### Run model

Train preliminary model

```{r}
# Tonality with tonal loudness

nperm <- 5

resultsOutTonal1 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutTonal1$OOB_RMSE
resultsOutTonal1$OOB_MAE
resultsOutTonal1$Rsquared
```
Train multiple seeds model

```{r}
# Tonality with tonal loudness

resultsOutTonal1 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutTonal1$OOB_RMSE
resultsOutTonal1$OOB_MAE
resultsOutTonal1$Rsquared

```

```{r}
# store results
resdAnnoyMnFitAB['Diff tonal inc loud', 'RMSE'] <- resultsOutTonal1$OOB_RMSE
resdAnnoyMnFitAB['Diff tonal inc loud', 'MAE'] <- resultsOutTonal1$OOB_MAE
resdAnnoyMnFitAB['Diff tonal inc loud', 'Rsquared'] <- resultsOutTonal1$Rsquared
resdAnnoyMnPermImpAB$DiffTonal1 <- resultsOutTonal1$conditional_permimp

```

###### Plot results

```{r, fig.width=8,fig.height=2.6}

par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutTonal1.conimp <- arrange(resultsOutTonal1$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutTonal1.conimp) + geom_col(aes(x=factor(rownames(resultsOutTonal1.conimp), levels=rownames(resultsOutTonal1.conimp)), y=CondPermImp), fill=mycolours[3], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (mean change in annoyance)") + ggtitle("dTonality inc. dtonal loudness") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 2.2))
pBar

if (saveplots){
  ggsave(filename="PtsABdAnnoyMndTonalLdConPermimp.svg", width=8, height=2.6, path=file.path(outFigPath, "svg"))
  unlink("PtsABdAnnoyMndTonalLdConPermimp.svg")
  
  ggsave(filename="PtsABdAnnoyMndTonalLdConPermimp.pdf", width=8, height=2.6, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdAnnoyMndTonalLdConPermimp.pdf")
}

```

Selected metric

```{r}

dTonLdVar <- "dTonLdECMAPowAvgBin"

```

##### dTonality without dtonal loudness

###### Set variables

```{r}

iVars <- c(diffVar, eventVar, "dTonalECMAAvgMaxLR", "dTonalSHMInt05ExMaxLR", "dTonalSHMIntAvgMaxLR", "dTonalECMA05ExMaxLR")
dVar <- "dAnnoyMean"

seeds <- c(410865, 2954, 70812, 203, 7984)

```

###### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```

Selected hyperparameters

```{r}

ntree <- 1501
mtry <- as.integer(length(iVars)/1.75)

```

###### Run model

Train preliminary model

```{r}
# Tonality

nperm <- 5

resultsOutTonal2 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
                           ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutTonal2$OOB_RMSE
resultsOutTonal2$OOB_MAE
resultsOutTonal2$Rsquared

```

Train multiple seeds model

```{r}
# Tonality

resultsOutTonal2 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutTonal2$OOB_RMSE
resultsOutTonal2$OOB_MAE
resultsOutTonal2$Rsquared

```


```{r}

# store results
resdAnnoyMnFitAB['Diff tonal no loud', 'RMSE'] <- resultsOutTonal2$OOB_RMSE
resdAnnoyMnFitAB['Diff tonal no loud', 'MAE'] <- resultsOutTonal2$OOB_MAE
resdAnnoyMnFitAB['Diff tonal no loud', 'Rsquared'] <- resultsOutTonal2$Rsquared
resdAnnoyMnPermImpAB$DiffTonal2 <- resultsOutTonal2$conditional_permimp

```

###### Plot results

```{r, fig.width=8,fig.height=2}
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutTonal2.conimp <- arrange(resultsOutTonal2$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutTonal2.conimp) + geom_col(aes(x=factor(rownames(resultsOutTonal2.conimp), levels=rownames(resultsOutTonal2.conimp)), y=CondPermImp), fill=mycolours[3], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (mean change in annoyance)") + ggtitle("dTonality w/o tonal loudness") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 2.2))
pBar

if (saveplots){
  ggsave(filename="PtsABdAnnoyMndTonalConPermimp.svg", width=8, height=2, path=file.path(outFigPath, "svg"))
  unlink("PtsABdAnnoyMndTonalConPermimp.svg")
  
  ggsave(filename="PtsABdAnnoyMndTonalConPermimp.pdf", width=8, height=2, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdAnnoyMndTonalConPermimp.pdf")
}


```

Selectec metric

```{r}

dTonalVar <- "dTonalSHMIntAvgMaxLR"

```

##### dFluctuation strength

###### Set variables

```{r}

# Fluctuation strength
iVars <- c(diffVar, eventVar, "dFluctSHM10ExBin", "dFluctSHM05ExBin", "dFluctOV10ExMaxLR", "dFluctOV05ExMaxLR")
dVar <- "dAnnoyMean"

seeds <- c(418657, 84, 1630, 18659, 3687)

```


###### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```

Selected hyperparameters

```{r}

ntree <- 2501
mtry <- as.integer(length(iVars)/1.75)

```

###### Run model

Train preliminary model

```{r}

nperm <- 5

resultsOutFluct <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutFluct$OOB_RMSE
resultsOutFluct$OOB_MAE
resultsOutFluct$Rsquared

```
Train multiple seeds model

```{r}

resultsOutFluct <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutFluct$OOB_RMSE
resultsOutFluct$OOB_MAE
resultsOutFluct$Rsquared

```


```{r}

# store results
resdAnnoyMnFitAB['Diff fluct', 'RMSE'] <- resultsOutFluct$OOB_RMSE
resdAnnoyMnFitAB['Diff fluct', 'MAE'] <- resultsOutFluct$OOB_MAE
resdAnnoyMnFitAB['Diff fluct', 'Rsquared'] <- resultsOutFluct$Rsquared
resdAnnoyMnPermImpAB$DiffFluct <- resultsOutFluct$conditional_permimp

```

###### Plot results

```{r, fig.width=8,fig.height=2}
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutFluct.conimp <- arrange(resultsOutFluct$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutFluct.conimp) + geom_col(aes(x=factor(rownames(resultsOutFluct.conimp), levels=rownames(resultsOutFluct.conimp)), y=CondPermImp), fill=mycolours[4], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (mean change in annoyance)") + ggtitle("dFluctuation strength") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename="PtsABdAnnoyMndFluctConPermimp.svg", width=8, height=2, path=file.path(outFigPath, "svg"))
  unlink("PtsABdAnnoyMnFluctConPermimp.svg")
  
  ggsave(filename="PtsABdAnnoyMndFluctConPermimp.pdf", width=8, height=2, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdAnnoyMnFluctConPermimp.pdf")
}

```

Selected metric

```{r}

dFluctVar <- "dFluctSHM05ExBin"

```


##### dRoughness

###### Set variables

```{r}

# Roughness
iVars <- c(diffVar, eventVar, "dRoughECMA10ExBin", "dRoughECMA05ExBin", "dRoughFZ10ExMaxLR", "dRoughFZ05ExMaxLR")
dVar <- "dAnnoyMean"

seeds <- c(69851, 85109, 410986, 1563, 896)

```

###### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```

Selected hyperparameters

```{r}

ntree <- 5501
mtry <- as.integer(length(iVars)/1.75)

```

###### Run model

Train preliminary model

```{r}

nperm <- 5

resultsOutRough <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutRough$OOB_RMSE
resultsOutRough$OOB_MAE
resultsOutRough$Rsquared

```

Train multiple seeds model

```{r}

resultsOutRough <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutRough$OOB_RMSE
resultsOutRough$OOB_MAE
resultsOutRough$Rsquared

```

```{r}
# store results
resdAnnoyMnFitAB['Diff rough', 'RMSE'] <- resultsOutRough$OOB_RMSE
resdAnnoyMnFitAB['Diff rough', 'MAE'] <- resultsOutRough$OOB_MAE
resdAnnoyMnFitAB['Diff rough', 'Rsquared'] <- resultsOutRough$Rsquared
resdAnnoyMnPermImpAB$DiffRough <- resultsOutRough$conditional_permimp

```

###### Plot results

```{r, fig.width=8,fig.height=2}
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutRough.conimp <- arrange(resultsOutRough$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutRough.conimp) + geom_col(aes(x=factor(rownames(resultsOutRough.conimp), levels=rownames(resultsOutRough.conimp)), y=CondPermImp), fill=mycolours[5], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (mean change in annoyance)") + ggtitle("dRoughness") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename="PtsABdAnnoyMndRoughConPermimp.svg", width=8, height=2, path=file.path(outFigPath, "svg"))
  unlink("PtsABdAnnoyMndRoughConPermimp.svg")
  
  ggsave(filename="PtsABdAnnoyMndRoughConPermimp.pdf", width=8, height=2, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdAnnoyMndRoughConPermimp.pdf")
}


```

Selected metric

```{r}

dRoughVar <- "dRoughECMA05ExBin"

```

##### dImpulsiveness

###### Set variables

```{r}
# Impulsiveness
iVars <- c(diffVar, eventVar, "dImpulsSHMAvgMaxLR", "dImpulsSHM05ExMaxLR", "dImpulsSHMPowAvgMaxLR")
dVar <- "dAnnoyMean"

seeds <- c(418659, 7805, 38475, 65834, 1653)

```

###### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```

Selected hyperparameters

```{r}

ntree <- 2501
mtry <- as.integer(length(iVars)/1.25)

```

###### Run model

Train preliminary model

```{r}

nperm <- 5

resultsOutImpuls <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutImpuls$OOB_RMSE
resultsOutImpuls$OOB_MAE
resultsOutImpuls$Rsquared

```

Train multiple seeds model

```{r}

resultsOutImpuls <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutImpuls$OOB_RMSE
resultsOutImpuls$OOB_MAE
resultsOutImpuls$Rsquared

```

```{r}

# store results
resdAnnoyMnFitAB['Diff impuls', 'RMSE'] <- resultsOutImpuls$OOB_RMSE
resdAnnoyMnFitAB['Diff impuls', 'MAE'] <- resultsOutImpuls$OOB_MAE
resdAnnoyMnFitAB['Diff impuls', 'Rsquared'] <- resultsOutImpuls$Rsquared
resdAnnoyMnPermImpAB$DiffImpuls <- resultsOutImpuls$conditional_permimp

```

###### Plot results

```{r, fig.width=8,fig.height=2}
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutImpuls.conimp <- arrange(resultsOutImpuls$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutImpuls.conimp) + geom_col(aes(x=factor(rownames(resultsOutImpuls.conimp), levels=rownames(resultsOutImpuls.conimp)), y=CondPermImp), fill=mycolours[6], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (mean change in annoyance)") + ggtitle("dImpulsiveness") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename="PtsABdAnnoyMndImpulsConPermimp.svg", width=8, height=2, path=file.path(outFigPath, "svg"))
  unlink("PtsABdAnnoyMndImpulsConPermimp.svg")
  
  ggsave(filename="PtsABdAnnoyMndImpulsConPermimp.pdf", width=8, height=2, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdAnnoyMndImpulsConPermimp.pdf")
}

```

Selected metric

```{r}

dImpulsVar <- "dImpulsSHM05ExMaxLR"

```

#### dSQM and loudness comparison

Now the highest importance dSQMs are ranked against each other, controlling for loudness difference.

##### Include dtonal loudness

###### Set variables

```{r}

iVars <- c(diffVar, eventVar, dSharpVar, dTonLdVar, dFluctVar, dRoughVar, dImpulsVar)
dVar <- "dAnnoyMean"

seeds <- c(98465, 54163, 6541, 36485, 849675)

```

###### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```

Selected hyperparameters

```{r}

ntree <- 251
mtry <- as.integer(length(iVars)/1.25)

```

###### Run model

Train preliminary model

```{r}

nperm <- 5

resultsOutSQMs1 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSQMs1$OOB_RMSE
resultsOutSQMs1$OOB_MAE
resultsOutSQMs1$Rsquared

```

Train multiple seeds model

```{r}

resultsOutSQMs1 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSQMs1$OOB_RMSE
resultsOutSQMs1$OOB_MAE
resultsOutSQMs1$Rsquared

```

```{r}

# store results
resdAnnoyMnFitAB['Diff SQMs inc tonal loud', 'RMSE'] <- resultsOutSQMs1$OOB_RMSE
resdAnnoyMnFitAB['Diff SQMs inc tonal loud', 'MAE'] <- resultsOutSQMs1$OOB_MAE
resdAnnoyMnFitAB['Diff SQMs inc tonal loud', 'Rsquared'] <- resultsOutSQMs1$Rsquared
resdAnnoyMnPermImpAB$DiffSQMs1 <- resultsOutSQMs1$conditional_permimp

```

###### Plot results

```{r, fig.width=8,fig.height=2.4}
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutSQMs1.conimp <- arrange(resultsOutSQMs1$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutSQMs1.conimp) + geom_col(aes(x=factor(rownames(resultsOutSQMs1.conimp), levels=rownames(resultsOutSQMs1.conimp)), y=CondPermImp), fill=mycolours[7], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (mean change in annoyance)") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 2.4))
pBar

if (saveplots){
  ggsave(filename="PtsABdAnnoyMnDiffSQMsTonLdConPermimp.svg", width=8, height=2.4, path=file.path(outFigPath, "svg"))
  unlink("PtsABdAnnoyMnDiffSQMsTonLdConPermimp.svg")
  
  ggsave(filename="PtsABdAnnoyMnDiffSQMsTonLdConPermimp.pdf", width=8, height=2.4, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdAnnoyMnDiffSQMsTonLdConPermimp.pdf")
}

```

##### Exclude tonal loudness

###### Set variables

```{r}

iVars <- c(diffVar, eventVar, dSharpVar, dTonalVar, dFluctVar, dRoughVar, dImpulsVar)
dVar <- "dAnnoyMean"

seeds <- c(49865, 7852, 845961, 410583, 36748)

```

###### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```

Selected hyperparameters

```{r}

ntree <- 5501
mtry <- as.integer(length(iVars)/1.25)

```


###### Run model

Train preliminary model

```{r}

nperm <- 5

resultsOutSQMs2 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSQMs2$OOB_RMSE
resultsOutSQMs2$OOB_MAE
resultsOutSQMs2$Rsquared

```

Train multiple seeds model

```{r}

resultsOutSQMs2 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSQMs2$OOB_RMSE
resultsOutSQMs2$OOB_MAE
resultsOutSQMs2$Rsquared

```

```{r}

# store results
resdAnnoyMnFitAB['Diff SQMs no tonal loud', 'RMSE'] <- resultsOutSQMs2$OOB_RMSE
resdAnnoyMnFitAB['Diff SQMs no tonal loud', 'MAE'] <- resultsOutSQMs2$OOB_MAE
resdAnnoyMnFitAB['Diff SQMs no tonal loud', 'Rsquared'] <- resultsOutSQMs2$Rsquared
resdAnnoyMnPermImpAB$DiffSQMs2 <- resultsOutSQMs2$conditional_permimp

```

###### Plot results

```{r, fig.width=8,fig.height=2.4}
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutSQMs2.conimp <- arrange(resultsOutSQMs2$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutSQMs2.conimp) + geom_col(aes(x=factor(rownames(resultsOutSQMs2.conimp), levels=rownames(resultsOutSQMs2.conimp)), y=CondPermImp), fill=mycolours[7], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (mean change in annoyance)") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 2.4))
pBar

if (saveplots){
  ggsave(filename="PtsABdAnnoyMnDiffSQMsNoTonLdConPermimp.svg", width=8, height=2.4, path=file.path(outFigPath, "svg"))
  unlink("PtsABdAnnoyMnDiffSQMsNoTonLdConPermimp.svg")
  
  ggsave(filename="PtsABdAnnoyMnDiffSQMsNoTonLdConPermimp.pdf", width=8, height=2.4, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdAnnoyMnDiffSQMsNoTonLdConPermimp.pdf")
}

```

### Save the results outputs to file

```{r}

if (savedata){
  utils::write.csv(resdAnnoyMnFitAB, paste(outDataPath, "\\PtsABCRFdAnnoyMnOOBFit.csv", sep=""))
  ii <- 0
  temp = list()
  for (res in resdAnnoyMnPermImpAB){
    ii <- ii + 1
    temp[[ii]] <- as.data.frame(resdAnnoyMnPermImpAB[ii])
    names(temp[[ii]]) <- names(resdAnnoyMnPermImpAB[ii])
  }
  openxlsx::write.xlsx(temp, paste(outDataPath, "\\PtsABCRFdAnnoyMnConPermimp.xlsx",
                                   sep=""),
                       rowNames=TRUE)
}

```

## (Change to) High annoyance

### Initialise results output variables

```{r}
resdHiAnnoyFitAB <- data.frame(RMSE = numeric(),
                             MAE = numeric(),
                             Rsquared = numeric())
resdHiAnnoyPermImpAB <- list()

```

### All variables (absolute and difference)

#### Set variables

```{r}

iVars <- names(stimDataNum)[which(names(stimDataNum) == 'UASEvents'):which(names(stimDataNum) == 'UASImpulsSHM05ExMaxLR')]
iVars <- iVars[! iVars %in% 'SNRlevel']
iVars <- c(iVars,
           names(stimDataNum)[which(colnames(stimDataNum)=="LAeqLAF90diff"):
                               which(colnames(stimDataNum)=="dImpulsSHM05ExMaxLR")], "SNRlevel")
dVar <- "dHighAnnoyPc"

seeds <- c(2, 312, 1897, 465978, 821659)

```

#### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
              ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```

Selected hyperparameters

```{r}

ntree <- 1501
mtry <- as.integer(length(iVars)/3.5)

```

#### Run model

Train preliminary model

```{r}

nperm <- 5

resultsOutAbsDiffs <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutAbsDiffs$OOB_RMSE
resultsOutAbsDiffs$OOB_MAE
resultsOutAbsDiffs$Rsquared

```

Train multiple seeds model

```{r}

resultsOutAbsDiffs <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutAbsDiffs$OOB_RMSE
resultsOutAbsDiffs$OOB_MAE
resultsOutAbsDiffs$Rsquared
```

```{r}
# store results
resdHiAnnoyFitAB['All vars', 'RMSE'] <- resultsOutAbsDiffs$OOB_RMSE
resdHiAnnoyFitAB['All vars', 'MAE'] <- resultsOutAbsDiffs$OOB_MAE
resdHiAnnoyFitAB['All vars', 'Rsquared'] <- resultsOutAbsDiffs$Rsquared
resdHiAnnoyPermImpAB$AllVars <- resultsOutAbsDiffs$conditional_permimp

```

#### Plot results

```{r, fig.width=8,fig.height=20}
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutAbsDiffs.conimp <- arrange(resultsOutAbsDiffs$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutAbsDiffs.conimp) + geom_col(aes(x=factor(rownames(resultsOutAbsDiffs.conimp), levels=rownames(resultsOutAbsDiffs.conimp)), y=CondPermImp), fill=mycolours[9], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (% highly annoyed)") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename="PtsABdHiAnnoyAllVarsConPermimp.svg", width=8, height=20, path=file.path(outFigPath, "svg"))
  unlink("PtsABdHiAnnoyAllVarsConPermimp.svg")

  ggsave(filename="PtsABdHiAnnoyAllVarsConPermimp.pdf", width=8, height=20, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdHiAnnoyAllVarsConPermimp.pdf")
}

```

```{r, fig.width=8,fig.height=15}

# Plot only positive values

resultsOutAbsDiffs.conimpPtv <- resultsOutAbsDiffs.conimp |>
                                          rownames_to_column('Metric') |>
                                                filter_if(is.numeric, all_vars(. > 0)) |>
                                                      column_to_rownames('Metric')

pBar <- ggplot(resultsOutAbsDiffs.conimpPtv) + geom_col(aes(x=factor(rownames(resultsOutAbsDiffs.conimpPtv), levels=rownames(resultsOutAbsDiffs.conimpPtv)), y=CondPermImp), fill=mycolours[9], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (% highly annoyed)") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename="PtsABdHiAnnoyAllVarsConPermimpPtv.svg", width=8, height=15, path=file.path(outFigPath, "svg"))
  unlink("PtsABdHiAnnoyAllVarsConPermimp.svg")
  
  ggsave(filename="PtsABdHiAnnoyAllVarsConPermimpPtv.pdf", width=8, height=15, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdHiAnnoyAllVarsConPermimp.pdf")
}

```


```{r, fig.width=8,fig.height=7}

# Plot only values within 1% of the maximum

resultsOutAbsDiffs.conimp1pc <- resultsOutAbsDiffs.conimp |>
                                          rownames_to_column('Metric') |>
                                                filter_if(is.numeric, all_vars(. > max(resultsOutAbsDiffs.conimp)/100)) |>
                                                      column_to_rownames('Metric')

pBar <- ggplot(resultsOutAbsDiffs.conimp1pc) + geom_col(aes(x=factor(rownames(resultsOutAbsDiffs.conimp1pc), levels=rownames(resultsOutAbsDiffs.conimp1pc)), y=CondPermImp), fill=mycolours[9], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (% highly annoyed)") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename="PtsABdHiAnnoyAllVarsConPermimp1pc.svg", width=8, height=7, path=file.path(outFigPath, "svg"))
  unlink("PtsABdHiAnnoyAllVarsConPermimp1pc.svg")
  
  ggsave(filename="PtsABdHiAnnoyAllVarsConPermimp1pc.pdf", width=8, height=7, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdHiAnnoyAllVarsConPermimp1pc.pdf")
}

```


### Absolute variables

#### Set variables

```{r}

iVars <- names(stimDataNum)[which(names(stimDataNum) == 'UASEvents'):which(names(stimDataNum) == 'UASImpulsSHM05ExMaxLR')]
iVars <- iVars[! iVars %in% c('SNRlevel', 'IntermitRatioC2MaxLR', 'IntermitRatioC3MaxLR', 'IntermitRatioC5MaxLR')]
dVar <- "dHighAnnoyPc"

seeds <- c(578312, 544, 84894, 54654, 153157)

```

#### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

if (saveplots){
  ggsave(filename="PtsABdHiAnnoyAbsVarsHyperTune.svg", width=12, height=4, path=file.path(outFigPath, "svg"))
  unlink("PtsABdHiAnnoyAbsVarsHyperTune.svg")

  ggsave(filename="PtsABdHiAnnoyAbsVarsHyperTune.pdf", width=12, height=4, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdHiAnnoyAbsVarsHyperTune.pdf")
}

```

Selected hyperparameters

```{r}

ntree <- 501
mtry <- as.integer(length(iVars)/1.75)

```

#### Run model

Train preliminary model

```{r}

nperm <- 10

resultsOutAbs <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutAbs$OOB_RMSE
resultsOutAbs$OOB_MAE
resultsOutAbs$Rsquared

```
Train multiple seeds model

```{r}

resultsOutAbs <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutAbs$OOB_RMSE
resultsOutAbs$OOB_MAE
resultsOutAbs$Rsquared

```


```{r}

# store results
resdHiAnnoyFitAB['Abs vars', 'RMSE'] <- resultsOutAbs$OOB_RMSE
resdHiAnnoyFitAB['Abs vars', 'MAE'] <- resultsOutAbs$OOB_MAE
resdHiAnnoyFitAB['Abs vars', 'Rsquared'] <- resultsOutAbs$Rsquared
resdHiAnnoyPermImpAB$AbsVars <- resultsOutAbs$conditional_permimp

```

#### Plot results

```{r, fig.width=8,fig.height=11.5}
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutAbs.conimp <- arrange(resultsOutAbs$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutAbs.conimp) + geom_col(aes(x=factor(rownames(resultsOutAbs.conimp), levels=rownames(resultsOutAbs.conimp)), y=CondPermImp), fill=mycolours[1], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (% highly annoyed)") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) +
  coord_flip()
pBar

if (saveplots){
  ggsave(filename="PtsABdHiAnnoyAbsVarsConPermimp.svg", width=8, height=11.5, path=file.path(outFigPath, "svg"))
  unlink("PtsABdHiAnnoyAbsVarsConPermimp.svg")
  
  ggsave(filename="PtsABdHiAnnoyAbsVarsConPermimp.pdf", width=8, height=11.5, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdHiAnnoyAbsVarsConPermimp.pdf")
}
```

```{r, fig.width=8,fig.height=8}

# Plot only positive values
resultsOutAbs.conimpPtv <- resultsOutAbs.conimp |>
                                          rownames_to_column('Metric') |>
                                                filter_if(is.numeric, all_vars(. > 0)) |>
                                                      column_to_rownames('Metric')

pBar <- ggplot(resultsOutAbs.conimpPtv,) + geom_col(aes(x=factor(rownames(resultsOutAbs.conimpPtv), levels=rownames(resultsOutAbs.conimpPtv)), y=CondPermImp), fill=mycolours[1], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (% highly annoyed)") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename="PtsABdHiAnnoyAbsVarsConPermimpPtv.svg", width=8, height=8, path=file.path(outFigPath, "svg"))
  unlink("PtsABdHiAnnoyAbsVarsConPermimpPtv.svg")
  
  ggsave(filename="PtsABdHiAnnoyAbsVarsConPermimpPtv.pdf", width=8, height=8, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdHiAnnoyAbsVarsConPermimpPtv.pdf")
}

```


```{r, fig.width=8,fig.height=4}

# Plot only values within 1% of the maximum
resultsOutAbs.conimp1pc <- resultsOutAbs.conimp |>
                                          rownames_to_column('Metric') |>
                                                filter_if(is.numeric, all_vars(. > max(resultsOutAbs.conimp)/100)) |>
                                                      column_to_rownames('Metric')

pBar <- ggplot(resultsOutAbs.conimp1pc,) + geom_col(aes(x=factor(rownames(resultsOutAbs.conimp1pc), levels=rownames(resultsOutAbs.conimp1pc)), y=CondPermImp), fill=mycolours[1], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (% highly annoyed)") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename="PtsABdHiAnnoyAbsVarsConPermimp1pc.svg", width=8, height=4, path=file.path(outFigPath, "svg"))
  unlink("PtsABdHiAnnoyAbsVarsConPermimp1pc.svg")
  
  ggsave(filename="PtsABdHiAnnoyAbsVarsConPermimp1pc.pdf", width=8, height=4, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdHiAnnoyAbsVarsConPermimp1pc.pdf")
}

```

Selected metric

```{r}

absVar <- "UASLoudECMAPowAvgBin"

```

### SQM analysis

#### Replace ambient only stimuli - OMITTED

Here, the 'ambient only' stimuli are replaced, as the SQM analysis can incorporate 0 values (whereas dB metrics cannot be handled in this way).

```{r}
# 
# stimDataNum <- rbind(stimDataNum, stimData[stimData['Stimulus']
#                                               == "A1",
#                                               colnames(stimDataNum)],
#                       stimData[stimData['Stimulus']
#                                               == "A2",
#                                               colnames(stimDataNum)])
# stimDataNum <- arrange(stimDataNum, Stimulus)

```

#### Individual SQMs

##### Sharpness

###### Set variables

```{r}

iVars <- c(absVar, eventVar, ambVar, "UASSharpAurISO3PowAvgMaxLR", "UASSharpAurISO305ExMaxLR", "UASSharpAurSHMPowAvgMaxLR", "UASSharpAurSHM05ExMaxLR", "UASSharpAurISO1PowAvgMaxLR", "UASSharpAurISO105ExMaxLR", "UASSharpvBISO1PowAvgMaxLR", "UASSharpvBISO105ExMaxLR", "UASSharpDINPowAvgMaxLR", "UASSharpDIN05ExMaxLR")
dVar <- "dHighAnnoyPc"

seeds <- c(7041, 905, 4984651, 6513213, 120651)

```

###### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p



```

Selected hyperparameters

```{r}

ntree <- 1001
mtry <- as.integer(length(iVars)/1.5)

```

###### Run model

Train preliminary model

```{r}

nperm <- 5

resultsOutSharp <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSharp$OOB_RMSE
resultsOutSharp$OOB_MAE
resultsOutSharp$Rsquared

```
Train multiple seeds model

```{r}

resultsOutSharp <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSharp$OOB_RMSE
resultsOutSharp$OOB_MAE
resultsOutSharp$Rsquared

```


```{r}
# store results
resdHiAnnoyFitAB['Abs sharp', 'RMSE'] <- resultsOutSharp$OOB_RMSE
resdHiAnnoyFitAB['Abs sharp', 'MAE'] <- resultsOutSharp$OOB_MAE
resdHiAnnoyFitAB['Abs sharp', 'Rsquared'] <- resultsOutSharp$Rsquared
resdHiAnnoyPermImpAB$AbsSharp <- resultsOutSharp$conditional_permimp

```

###### Plot results

```{r, fig.width=8,fig.height=4.9}
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutSharp.conimp <- arrange(resultsOutSharp$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutSharp.conimp) + geom_col(aes(x=factor(rownames(resultsOutSharp.conimp), levels=rownames(resultsOutSharp.conimp)), y=CondPermImp), fill=mycolours[2], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (% highly annoyed)") + ggtitle("Sharpness") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename="PtsABdHiAnnoySharpConPermimp.svg", width=8, height=4.9, path=file.path(outFigPath, "svg"))
  unlink("PtsABdHiAnnoySharpConPermimp.svg")
  
  ggsave(filename="PtsABdHiAnnoySharpConPermimp.pdf", width=8, height=4.9, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdHiAnnoySharpConPermimp.pdf")
}


```

Selected metric

```{r}

sharpVar <- "UASSharpAurISO305ExMaxLR"

```


##### Tonal loudness and tonality

###### Set variables

```{r}

iVars <- c(absVar, eventVar, ambVar, "UASTonalECMAAvgMaxLR", "UASTonalSHMInt05ExMaxLR", "UASTonalSHMIntAvgMaxLR", "UASTonalECMA05ExMaxLR", "UASTonLdECMAPowAvgBin", "UASTonLdECMA05ExBin", "UASTonalAurAvgMaxLR", "UASTonalAur05ExMaxLR", "UASTonalAur10ExMaxLR")
dVar <- "dHighAnnoyPc"

seeds <- c(540, 104798, 456464, 87331, 94564)

```

###### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```

Selected hyperparameters

```{r}

ntree <- 501
mtry <- as.integer(length(iVars)/1.25)

```

###### Run model

Train preliminary model

```{r}
# Tonality with tonal loudness

nperm <- 5

resultsOutTonal1 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutTonal1$OOB_RMSE
resultsOutTonal1$OOB_MAE
resultsOutTonal1$Rsquared
```

Train multiple seeds model

```{r}
# Tonality with tonal loudness

resultsOutTonal1 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutTonal1$OOB_RMSE
resultsOutTonal1$OOB_MAE
resultsOutTonal1$Rsquared

```

```{r}
# store results
resdHiAnnoyFitAB['Abs tonal inc loud', 'RMSE'] <- resultsOutTonal1$OOB_RMSE
resdHiAnnoyFitAB['Abs tonal inc loud', 'MAE'] <- resultsOutTonal1$OOB_MAE
resdHiAnnoyFitAB['Abs tonal inc loud', 'Rsquared'] <- resultsOutTonal1$Rsquared
resdHiAnnoyPermImpAB$AbsTonal1 <- resultsOutTonal1$conditional_permimp

```

###### Plot results

```{r, fig.width=8,fig.height=3.8}

par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutTonal1.conimp <- arrange(resultsOutTonal1$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutTonal1.conimp) + geom_col(aes(x=factor(rownames(resultsOutTonal1.conimp), levels=rownames(resultsOutTonal1.conimp)), y=CondPermImp), fill=mycolours[3], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (% highly annoyed)") + ggtitle("Tonality inc. tonal loudness") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 110))
pBar

if (saveplots){
  ggsave(filename="PtsABdHiAnnoyTonalLdConPermimp.svg", width=8, height=3.8, path=file.path(outFigPath, "svg"))
  unlink("PtsABdHiAnnoyTonalLdConPermimp.svg")
  
  ggsave(filename="PtsABdHiAnnoyTonalLdConPermimp.pdf", width=8, height=3.8, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdHiAnnoyTonalLdConPermimp.pdf")
}

```

Selected metric

```{r}

tonLdVar <- "UASTonLdECMAPowAvgBin"

```


##### Tonality without tonal loudness

###### Set variables

```{r}

iVars <- c(absVar, eventVar, ambVar, "UASTonalECMAAvgMaxLR", "UASTonalSHMInt05ExMaxLR", "UASTonalSHMIntAvgMaxLR", "UASTonalECMA05ExMaxLR", "UASTonalAurAvgMaxLR", "UASTonalAur05ExMaxLR", "UASTonalAur10ExMaxLR")
dVar <- "dHighAnnoyPc"

seeds <- c(156089, 5860, 10528, 89541, 4685146)

```

###### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```

Selected hyperparameters

```{r}

ntree <- 5501
mtry <- as.integer(length(iVars)/1.25)

```


###### Run model

Train preliminary model

```{r}
# Tonality

nperm <- 5

resultsOutTonal2 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
                           ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm,
                           minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutTonal2$OOB_RMSE
resultsOutTonal2$OOB_MAE
resultsOutTonal2$Rsquared

```

Train multiple seeds model

```{r}
# Tonality

resultsOutTonal2 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutTonal2$OOB_RMSE
resultsOutTonal2$OOB_MAE
resultsOutTonal2$Rsquared

```


```{r}

# store results
resdHiAnnoyFitAB['Abs tonal no loud', 'RMSE'] <- resultsOutTonal2$OOB_RMSE
resdHiAnnoyFitAB['Abs tonal no loud', 'MAE'] <- resultsOutTonal2$OOB_MAE
resdHiAnnoyFitAB['Abs tonal no loud', 'Rsquared'] <- resultsOutTonal2$Rsquared
resdHiAnnoyPermImpAB$AbsTonal2 <- resultsOutTonal2$conditional_permimp

```

###### Plot results

```{r, fig.width=8,fig.height=3.2}
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutTonal2.conimp <- arrange(resultsOutTonal2$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutTonal2.conimp) + geom_col(aes(x=factor(rownames(resultsOutTonal2.conimp), levels=rownames(resultsOutTonal2.conimp)), y=CondPermImp), fill=mycolours[3], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (% highly annoyed)") + ggtitle("Tonality w/o tonal loudness") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 110))
pBar

if (saveplots){
  ggsave(filename="PtsABdHiAnnoyTonalConPermimp.svg", width=8, height=3.2, path=file.path(outFigPath, "svg"))
  unlink("PtsABdHiAnnoyTonalConPermimp.svg")
  
  ggsave(filename="PtsABdHiAnnoyTonalConPermimp.pdf", width=8, height=3.2, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdHiAnnoyTonalConPermimp.pdf")
}


```

Selected metric

```{r}

tonalVar <- "UASTonalSHMIntAvgMaxLR"

```

##### Fluctuation strength

###### Set variables

```{r}

# Fluctuation strength
iVars <- c(absVar, eventVar, ambVar, "UASFluctSHM10ExBin", "UASFluctSHM05ExBin", "UASFluctFZ10ExMaxLR", "UASFluctFZ05ExMaxLR", "UASFluctOV10ExMaxLR", "UASFluctOV05ExMaxLR")
dVar <- "dHighAnnoyPc"

seeds <- c(25107, 546098, 195, 5937, 102658)

```

###### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```

Selected hyperparameters

```{r}

ntree <- 501
mtry <- as.integer(length(iVars)/1.25)

```

###### Run model

Train preliminary model

```{r}

nperm <- 5

resultsOutFluct <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
                          ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres,
                          nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutFluct$OOB_RMSE
resultsOutFluct$OOB_MAE
resultsOutFluct$Rsquared

```
Train multiple seeds model

```{r}

resultsOutFluct <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutFluct$OOB_RMSE
resultsOutFluct$OOB_MAE
resultsOutFluct$Rsquared

```


```{r}

# store results
resdHiAnnoyFitAB['Abs fluct', 'RMSE'] <- resultsOutFluct$OOB_RMSE
resdHiAnnoyFitAB['Abs fluct', 'MAE'] <- resultsOutFluct$OOB_MAE
resdHiAnnoyFitAB['Abs fluct', 'Rsquared'] <- resultsOutFluct$Rsquared
resdHiAnnoyPermImpAB$AbsFluct <- resultsOutFluct$conditional_permimp

```

###### Plot results

```{r, fig.width=8,fig.height=2.9}
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutFluct.conimp <- arrange(resultsOutFluct$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutFluct.conimp) + geom_col(aes(x=factor(rownames(resultsOutFluct.conimp), levels=rownames(resultsOutFluct.conimp)), y=CondPermImp), fill=mycolours[4], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (% highly annoyed)") + ggtitle("Fluctuation strength") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename="PtsABdHiAnnoyFluctConPermimp.svg", width=8, height=2.9, path=file.path(outFigPath, "svg"))
  unlink("PtsABdHiAnnoyFluctConPermimp.svg")
  
  ggsave(filename="PtsABdHiAnnoyFluctConPermimp.pdf", width=8, height=2.9, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdHiAnnoyFluctConPermimp.pdf")
}

```

Selected metric

```{r}

fluctVar <- "UASFluctOV10ExMaxLR"

```

##### Roughness

###### Set variables

```{r}

# Roughness
iVars <- c(absVar, eventVar, ambVar, "UASRoughECMA10ExBin", "UASRoughECMA05ExBin", "UASRoughFZ10ExMaxLR", "UASRoughFZ05ExMaxLR", "UASRoughDW10ExMaxLR", "UASRoughDW05ExMaxLR")
dVar <- "dHighAnnoyPc"

seeds <- c(4701, 52187, 16589, 65217, 16893)

```

###### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```

Selected hyperparameters

```{r}

ntree <- 2501
mtry <- as.integer(length(iVars)/1.25)

```


###### Run model

Train preliminary model

```{r}

nperm <- 5

resultsOutRough <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutRough$OOB_RMSE
resultsOutRough$OOB_MAE
resultsOutRough$Rsquared

```

Train multiple seeds model

```{r}

resultsOutRough <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutRough$OOB_RMSE
resultsOutRough$OOB_MAE
resultsOutRough$Rsquared

```

```{r}
# store results
resdHiAnnoyFitAB['Abs rough', 'RMSE'] <- resultsOutRough$OOB_RMSE
resdHiAnnoyFitAB['Abs rough', 'MAE'] <- resultsOutRough$OOB_MAE
resdHiAnnoyFitAB['Abs rough', 'Rsquared'] <- resultsOutRough$Rsquared
resdHiAnnoyPermImpAB$AbsRough <- resultsOutRough$conditional_permimp

```

###### Plot results

```{r, fig.width=8,fig.height=2.9}
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutRough.conimp <- arrange(resultsOutRough$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutRough.conimp) + geom_col(aes(x=factor(rownames(resultsOutRough.conimp), levels=rownames(resultsOutRough.conimp)), y=CondPermImp), fill=mycolours[5], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (% highly annoyed)") + ggtitle("Roughness") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename="PtsABdHiAnnoyRoughConPermimp.svg", width=8, height=2.9, path=file.path(outFigPath, "svg"))
  unlink("PtsABdHiAnnoyRoughConPermimp.svg")
  
  ggsave(filename="PtsABdHiAnnoyRoughConPermimp.pdf", width=8, height=2.9, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdHiAnnoyRoughConPermimp.pdf")
}


```

Selected metric

```{r}

roughVar <- "UASRoughFZ05ExMaxLR"

```


##### Impulsiveness

###### Set variables

```{r}
# Impulsiveness
iVars <- c(absVar, eventVar, ambVar, "UASImpulsSHMAvgMaxLR", "UASImpulsSHM05ExMaxLR", "UASImpulsSHMPowAvgMaxLR")
dVar <- "dHighAnnoyPc"

seeds <- c(8495, 59867, 5416, 9843, 86)

```

###### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```

Selected hyperparameters

```{r}

ntree <- 501
mtry <- as.integer(length(iVars)/1.75)

```


###### Run model

Train preliminary model

```{r}

nperm <- 5

resultsOutImpuls <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutImpuls$OOB_RMSE
resultsOutImpuls$OOB_MAE
resultsOutImpuls$Rsquared

```

Train multiple seeds model

```{r}

resultsOutImpuls <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutImpuls$OOB_RMSE
resultsOutImpuls$OOB_MAE
resultsOutImpuls$Rsquared

```

```{r}

# store results
resdHiAnnoyFitAB['Abs impuls', 'RMSE'] <- resultsOutImpuls$OOB_RMSE
resdHiAnnoyFitAB['Abs impuls', 'MAE'] <- resultsOutImpuls$OOB_MAE
resdHiAnnoyFitAB['Abs impuls', 'Rsquared'] <- resultsOutImpuls$Rsquared
resdHiAnnoyPermImpAB$AbsImpuls <- resultsOutImpuls$conditional_permimp

```

###### Plot results

```{r, fig.width=8,fig.height=2}
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutImpuls.conimp <- arrange(resultsOutImpuls$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutImpuls.conimp) + geom_col(aes(x=factor(rownames(resultsOutImpuls.conimp), levels=rownames(resultsOutImpuls.conimp)), y=CondPermImp), fill=mycolours[6], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (% highly annoyed)") + ggtitle("Impulsiveness") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename="PtsABdHiAnnoyImpulsConPermimp.svg", width=8, height=2, path=file.path(outFigPath, "svg"))
  unlink("PtsABdHiAnnoyImpulsConPermimp.svg")
  
  ggsave(filename="PtsABdHiAnnoyImpulsConPermimp.pdf", width=8, height=2, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdHiAnnoyImpulsConPermimp.pdf")
}

```

Selected metric

```{r}

impulsVar <- "UASImpulsSHMPowAvgMaxLR"

```

#### SQM and loudness comparison

Now the highest importance SQMs are ranked against each other, controlling for UAS loudness and ambient LAeq.

##### Include tonal loudness

###### Set variables

```{r}

iVars <- c(absVar, eventVar, ambVar, sharpVar, tonLdVar, fluctVar, roughVar, impulsVar)
dVar <- "dHighAnnoyPc"

seeds <- c(70498, 4, 14986, 453, 864)

```

###### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```

Selected hyperparameters

```{r}

ntree <- 1501
mtry <- as.integer(length(iVars)/1.25)

```


###### Run model

Train preliminary model

```{r}

nperm <- 5

resultsOutSQMs1 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSQMs1$OOB_RMSE
resultsOutSQMs1$OOB_MAE
resultsOutSQMs1$Rsquared

```

Train multiple seeds model

```{r}

resultsOutSQMs1 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSQMs1$OOB_RMSE
resultsOutSQMs1$OOB_MAE
resultsOutSQMs1$Rsquared

```

```{r}

# store results
resdHiAnnoyFitAB['Abs SQMs inc tonal loud', 'RMSE'] <- resultsOutSQMs1$OOB_RMSE
resdHiAnnoyFitAB['Abs SQMs inc tonal loud', 'MAE'] <- resultsOutSQMs1$OOB_MAE
resdHiAnnoyFitAB['Abs SQMs inc tonal loud', 'Rsquared'] <- resultsOutSQMs1$Rsquared
resdHiAnnoyPermImpAB$AbsSQMs1 <- resultsOutSQMs1$conditional_permimp

```

###### Plot results

```{r, fig.width=8,fig.height=2.4}
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutSQMs1.conimp <- arrange(resultsOutSQMs1$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutSQMs1.conimp) + geom_col(aes(x=factor(rownames(resultsOutSQMs1.conimp), levels=rownames(resultsOutSQMs1.conimp)), y=CondPermImp), fill=mycolours[7], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (% highly annoyed)") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 90))
pBar

if (saveplots){
  ggsave(filename="PtsABdHiAnnoyAbsSQMsTonLdConPermimp.svg", width=8, height=2.4, path=file.path(outFigPath, "svg"))
  unlink("PtsABdHiAnnoyAbsSQMsTonLdConPermimp.svg")
  
  ggsave(filename="PtsABdHiAnnoyAbsSQMsTonLdConPermimp.pdf", width=8, height=2.4, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdHiAnnoyAbsSQMsTonLdConPermimp.pdf")
}

```

##### Exclude tonal loudness

###### Set variables

```{r}

iVars <- c(absVar, eventVar, ambVar, sharpVar, tonalVar, fluctVar, roughVar, impulsVar)
dVar <- "dHighAnnoyPc"

seeds <- c(546, 57203, 270835, 60592, 8094)

```

###### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```

Selected hyperparameters

```{r}

ntree <- 1501
mtry <- as.integer(length(iVars)/1.25)

```


###### Run model

Train preliminary model

```{r}

nperm <- 5

resultsOutSQMs2 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSQMs2$OOB_RMSE
resultsOutSQMs2$OOB_MAE
resultsOutSQMs2$Rsquared

```

Train multiple seeds model

```{r}

resultsOutSQMs2 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSQMs2$OOB_RMSE
resultsOutSQMs2$OOB_MAE
resultsOutSQMs2$Rsquared

```

```{r}

# store results
resdHiAnnoyFitAB['Abs SQMs no tonal loud', 'RMSE'] <- resultsOutSQMs2$OOB_RMSE
resdHiAnnoyFitAB['Abs SQMs no tonal loud', 'MAE'] <- resultsOutSQMs2$OOB_MAE
resdHiAnnoyFitAB['Abs SQMs no tonal loud', 'Rsquared'] <- resultsOutSQMs2$Rsquared
resdHiAnnoyPermImpAB$AbsSQMs2 <- resultsOutSQMs2$conditional_permimp

```

###### Plot results

```{r, fig.width=8,fig.height=2.4}
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutSQMs2.conimp <- arrange(resultsOutSQMs2$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutSQMs2.conimp) + geom_col(aes(x=factor(rownames(resultsOutSQMs2.conimp), levels=rownames(resultsOutSQMs2.conimp)), y=CondPermImp), fill=mycolours[7], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (% highly annoyed)") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 90))
pBar

if (saveplots){
  ggsave(filename="PtsABdHiAnnoyAbsSQMsNoTonLdConPermimp.svg", width=8, height=2.4, path=file.path(outFigPath, "svg"))
  unlink("PtsABdHiAnnoyAbsSQMsNoTonLdConPermimp.svg")
  
  ggsave(filename="PtsABdHiAnnoyAbsSQMsNoTonLdConPermimp.pdf", width=8, height=2.4, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdHiAnnoyAbsSQMsNoTonLdConPermimp.pdf")
}

```

### Difference variables

Next, the difference metrics are analysed

#### Set variables

```{r}

iVars <- c(names(stimDataNum)[which(colnames(stimDataNum)=="LAeqLAF90diff"):
                               which(colnames(stimDataNum)=="dImpulsSHM05ExMaxLR")],
           "UASEvents", "SNRlevel", "IntermitRatioC2MaxLR", "IntermitRatioC3MaxLR", "IntermitRatioC5MaxLR")
dVar <- "dHighAnnoyPc"

seeds <- c(568392, 498, 4089, 78132, 741809)

```

#### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```

Selected hyperparameters

```{r}

ntree <- 4001
mtry <- as.integer(length(iVars)/2.25)

```

#### Run model

Train preliminary model

```{r}

nperm <- 5

resultsOutDiffs <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutDiffs$OOB_RMSE
resultsOutDiffs$OOB_MAE
resultsOutDiffs$Rsquared

```

Train multiple seeds model

```{r}

resultsOutDiffs <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutDiffs$OOB_RMSE
resultsOutDiffs$OOB_MAE
resultsOutDiffs$Rsquared

```

```{r}

# store results
resdHiAnnoyFitAB['Diff vars', 'RMSE'] <- resultsOutDiffs$OOB_RMSE
resdHiAnnoyFitAB['Diff vars', 'MAE'] <- resultsOutDiffs$OOB_MAE
resdHiAnnoyFitAB['Diff vars', 'Rsquared'] <- resultsOutDiffs$Rsquared
resdHiAnnoyPermImpAB$DiffVars <- resultsOutDiffs$conditional_permimp

```

#### Plot results

```{r, fig.width=8,fig.height=10}
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutDiffs.conimp <- arrange(resultsOutDiffs$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutDiffs.conimp) + geom_col(aes(x=factor(rownames(resultsOutDiffs.conimp), levels=rownames(resultsOutDiffs.conimp)), y=CondPermImp), fill=mycolours[8], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (% highly annoyed)") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename="PtsABdHiAnnoyDiffVarsConPermimp.svg", width=8, height=10, path=file.path(outFigPath, "svg"))
  unlink("PtsABdHiAnnoyDiffVarsConPermimp.svg")
  
  ggsave(filename="PtsABdHiAnnoyDiffVarsConPermimp.pdf", width=8, height=10, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdHiAnnoyDiffVarsConPermimp.pdf")
}

```
```{r, fig.width=8,fig.height=9}

# Plot only positive values
resultsOutDiffs.conimpPtv <- resultsOutDiffs.conimp |>
                                        rownames_to_column('Metric') |>
                                            filter_if(is.numeric, all_vars(. > 0)) |>
                                                  column_to_rownames('Metric')

pBar <- ggplot(resultsOutDiffs.conimpPtv) + geom_col(aes(x=factor(rownames(resultsOutDiffs.conimpPtv), levels=rownames(resultsOutDiffs.conimpPtv)), y=CondPermImp), fill=mycolours[8], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (% highly annoyed)") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename="PtsABdHiAnnoyDiffVarsConPermimpPtv.svg", width=8, height=9, path=file.path(outFigPath, "svg"))
  unlink("PtsABdHiAnnoyDiffVarsConPermimpPtv.svg")
  
  ggsave(filename="PtsABdHiAnnoyDiffVarsConPermimpPtv.pdf", width=8, height=9, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdHiAnnoyDiffVarsConPermimpPtv.pdf")
}

```

```{r, fig.width=8,fig.height=4}

# Plot only values within 1% of the maximum
resultsOutDiffs.conimp1pc <- resultsOutDiffs.conimp |>
                                        rownames_to_column('Metric') |>
                                            filter_if(is.numeric, all_vars(. > max(resultsOutDiffs.conimp)/100)) |>
                                                  column_to_rownames('Metric')

pBar <- ggplot(resultsOutDiffs.conimp1pc) + geom_col(aes(x=factor(rownames(resultsOutDiffs.conimp1pc), levels=rownames(resultsOutDiffs.conimp1pc)), y=CondPermImp), fill=mycolours[8], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (% highly annoyed)") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename="PtsABdHiAnnoyDiffVarsConPermimp1pc.svg", width=8, height=4, path=file.path(outFigPath, "svg"))
  unlink("PtsABdHiAnnoyDiffVarsConPermimp1pc.svg")
  
  ggsave(filename="PtsABdHiAnnoyDiffVarsConPermimp1pc.pdf", width=8, height=4, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdHiAnnoyDiffVarsConPermimp1pc.pdf")
}

```

Selected metric

```{r}

diffVar <- "LAELAeqdiff"

```

### dSQM analysis

The ambient-only stimuli are NOT replaced here, as the difference analysis includes dB metrics.

#### Individual SQMs

##### dSharpness

###### Set variables

```{r}

iVars <- c(diffVar, eventVar, "dSharpAurISO3PowAvgMaxLR", "dSharpAurISO305ExMaxLR", "dSharpAurSHMPowAvgMaxLR", "dSharpAurSHM05ExMaxLR")
dVar <- "dHighAnnoyPc"

seeds <- c(84194, 905, 64815, 928054, 625091, 582031)

```

###### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```

Selected hyperparameters

```{r}

ntree <- 251
mtry <- as.integer(length(iVars)/1.75)

```


###### Run model

Train preliminary model

```{r}

nperm <- 5

resultsOutSharp <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSharp$OOB_RMSE
resultsOutSharp$OOB_MAE
resultsOutSharp$Rsquared

```
Train multiple seeds model

```{r}

resultsOutSharp <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSharp$OOB_RMSE
resultsOutSharp$OOB_MAE
resultsOutSharp$Rsquared

```


```{r}
# store results
resdHiAnnoyFitAB['Diff sharp', 'RMSE'] <- resultsOutSharp$OOB_RMSE
resdHiAnnoyFitAB['Diff sharp', 'MAE'] <- resultsOutSharp$OOB_MAE
resdHiAnnoyFitAB['Diff sharp', 'Rsquared'] <- resultsOutSharp$Rsquared
resdHiAnnoyPermImpAB$DiffSharp <- resultsOutSharp$conditional_permimp

```

###### Plot results

```{r, fig.width=8,fig.height=2.6}
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutSharp.conimp <- arrange(resultsOutSharp$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutSharp.conimp) + geom_col(aes(x=factor(rownames(resultsOutSharp.conimp), levels=rownames(resultsOutSharp.conimp)), y=CondPermImp), fill=mycolours[2], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (% highly annoyed)") + ggtitle("dSharpness") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename="PtsABdHiAnnoydSharpConPermimp.svg", width=8, height=2.6, path=file.path(outFigPath, "svg"))
  unlink("PtsABdHiAnnoydSharpConPermimp.svg")
  
  ggsave(filename="PtsABdHiAnnoydSharpConPermimp.pdf", width=8, height=2.6, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdHiAnnoydSharpConPermimp.pdf")
}


```

Selected metric

```{r}

dSharpVar <- "dSharpAurSHMPowAvgMaxLR"

```


##### dTonal loudness and dtonality

###### Set variables

```{r}

iVars <- c(diffVar, eventVar, "dTonalECMAAvgMaxLR", "dTonalSHMInt05ExMaxLR", "dTonalSHMIntAvgMaxLR", "dTonalECMA05ExMaxLR", "dTonLdECMAPowAvgBin", "dTonLdECMA05ExBin")
dVar <- "dHighAnnoyPc"

```

###### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

seeds <- c(561684, 104798, 1536, 48, 48561)

```
Selected hyperparameters

```{r}

ntree <- 1001
mtry <- as.integer(length(iVars)/1.25)

```

###### Run model

Train preliminary model

```{r}
# Tonality with tonal loudness

nperm <- 5

resultsOutTonal1 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutTonal1$OOB_RMSE
resultsOutTonal1$OOB_MAE
resultsOutTonal1$Rsquared
```

Train multiple seeds model

```{r}
# Tonality with tonal loudness

resultsOutTonal1 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutTonal1$OOB_RMSE
resultsOutTonal1$OOB_MAE
resultsOutTonal1$Rsquared

```

```{r}
# store results
resdHiAnnoyFitAB['Diff tonal inc loud', 'RMSE'] <- resultsOutTonal1$OOB_RMSE
resdHiAnnoyFitAB['Diff tonal inc loud', 'MAE'] <- resultsOutTonal1$OOB_MAE
resdHiAnnoyFitAB['Diff tonal inc loud', 'Rsquared'] <- resultsOutTonal1$Rsquared
resdHiAnnoyPermImpAB$DiffTonal1 <- resultsOutTonal1$conditional_permimp

```

###### Plot results

```{r, fig.width=8,fig.height=2.6}

par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutTonal1.conimp <- arrange(resultsOutTonal1$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutTonal1.conimp) + geom_col(aes(x=factor(rownames(resultsOutTonal1.conimp), levels=rownames(resultsOutTonal1.conimp)), y=CondPermImp), fill=mycolours[3], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (% highly annoyed)") + ggtitle("dTonality inc. dtonal loudness") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 50))
pBar

if (saveplots){
  ggsave(filename="PtsABdHiAnnoydTonalLdConPermimp.svg", width=8, height=2.6, path=file.path(outFigPath, "svg"))
  unlink("PtsABdHiAnnoydTonalLdConPermimp.svg")
  
  ggsave(filename="PtsABdHiAnnoydTonalLdConPermimp.pdf", width=8, height=2.6, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdHiAnnoydTonalLdConPermimp.pdf")
}

```

Selected metric

```{r}

dTonLdVar <- "dTonLdECMAPowAvgBin"

```

##### dTonality without dtonal loudness

###### Set variables

```{r}

iVars <- c(diffVar, eventVar, "dTonalECMAAvgMaxLR", "dTonalSHMInt05ExMaxLR", "dTonalSHMIntAvgMaxLR", "dTonalECMA05ExMaxLR")
dVar <- "dHighAnnoyPc"

seeds <- c(410865, 2954, 70812, 203, 7984)

```

###### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p


```

Selected hyperparameters

```{r}

ntree <- 4001
mtry <- as.integer(length(iVars)/1.75)

```


###### Run model

Train preliminary model

```{r}
# Tonality

nperm <- 5

resultsOutTonal2 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutTonal2$OOB_RMSE
resultsOutTonal2$OOB_MAE
resultsOutTonal2$Rsquared

```

Train multiple seeds model

```{r}
# Tonality

resultsOutTonal2 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutTonal2$OOB_RMSE
resultsOutTonal2$OOB_MAE
resultsOutTonal2$Rsquared

```


```{r}

# store results
resdHiAnnoyFitAB['Diff tonal no loud', 'RMSE'] <- resultsOutTonal2$OOB_RMSE
resdHiAnnoyFitAB['Diff tonal no loud', 'MAE'] <- resultsOutTonal2$OOB_MAE
resdHiAnnoyFitAB['Diff tonal no loud', 'Rsquared'] <- resultsOutTonal2$Rsquared
resdHiAnnoyPermImpAB$DiffTonal2 <- resultsOutTonal2$conditional_permimp

```

###### Plot results

```{r, fig.width=8,fig.height=2}
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutTonal2.conimp <- arrange(resultsOutTonal2$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutTonal2.conimp) + geom_col(aes(x=factor(rownames(resultsOutTonal2.conimp), levels=rownames(resultsOutTonal2.conimp)), y=CondPermImp), fill=mycolours[3], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (% highly annoyed)") + ggtitle("dTonality w/o tonal loudness") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 50))
pBar

if (saveplots){
  ggsave(filename="PtsABdHiAnnoydTonalConPermimp.svg", width=8, height=2, path=file.path(outFigPath, "svg"))
  unlink("PtsABdHiAnnoydTonalConPermimp.svg")
  
  ggsave(filename="PtsABdHiAnnoydTonalConPermimp.pdf", width=8, height=2, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdHiAnnoydTonalConPermimp.pdf")
}


```

Selected metric

```{r}

dTonalVar <- "dTonalSHMIntAvgMaxLR"

```

##### dFluctuation strength

###### Set variables

```{r}

# Fluctuation strength
iVars <- c(diffVar, eventVar, "dFluctSHM10ExBin", "dFluctSHM05ExBin", "dFluctOV10ExMaxLR", "dFluctOV05ExMaxLR")
dVar <- "dHighAnnoyPc"

seeds <- c(418657, 84, 1630, 18659, 3687)

```


###### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```

Selected hyperparameters

```{r}

ntree <- 251
mtry <- as.integer(length(iVars)/1.75)

```

###### Run model

Train preliminary model

```{r}

nperm <- 5

resultsOutFluct <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutFluct$OOB_RMSE
resultsOutFluct$OOB_MAE
resultsOutFluct$Rsquared

```

Train multiple seeds model

```{r}

resultsOutFluct <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutFluct$OOB_RMSE
resultsOutFluct$OOB_MAE
resultsOutFluct$Rsquared

```


```{r}

# store results
resdHiAnnoyFitAB['Diff fluct', 'RMSE'] <- resultsOutFluct$OOB_RMSE
resdHiAnnoyFitAB['Diff fluct', 'MAE'] <- resultsOutFluct$OOB_MAE
resdHiAnnoyFitAB['Diff fluct', 'Rsquared'] <- resultsOutFluct$Rsquared
resdHiAnnoyPermImpAB$DiffFluct <- resultsOutFluct$conditional_permimp

```

###### Plot results

```{r, fig.width=8,fig.height=2}
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutFluct.conimp <- arrange(resultsOutFluct$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutFluct.conimp) + geom_col(aes(x=factor(rownames(resultsOutFluct.conimp), levels=rownames(resultsOutFluct.conimp)), y=CondPermImp), fill=mycolours[4], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (% highly annoyed)") + ggtitle("dFluctuation strength") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename="PtsABdHiAnnoydFluctConPermimp.svg", width=8, height=2, path=file.path(outFigPath, "svg"))
  unlink("PtsABdHiAnnoyFluctConPermimp.svg")
  
  ggsave(filename="PtsABdHiAnnoydFluctConPermimp.pdf", width=8, height=2, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdHiAnnoyFluctConPermimp.pdf")
}

```

Selected metric

```{r}

dFluctVar <- "dFluctSHM10ExBin"

```

##### dRoughness

###### Set variables

```{r}

# Roughness
iVars <- c(diffVar, eventVar, "dRoughECMA10ExBin", "dRoughECMA05ExBin", "dRoughFZ10ExMaxLR", "dRoughFZ05ExMaxLR")
dVar <- "dHighAnnoyPc"

seeds <- c(69851, 85109, 410986, 1563, 896)

```

###### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```

Selected hyperparameters

```{r}

ntree <- 251
mtry <- as.integer(length(iVars)/1.75)

```

###### Run model

Train preliminary model

```{r}

nperm <- 5

resultsOutRough <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutRough$OOB_RMSE
resultsOutRough$OOB_MAE
resultsOutRough$Rsquared

```

Train multiple seeds model

```{r}

resultsOutRough <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutRough$OOB_RMSE
resultsOutRough$OOB_MAE
resultsOutRough$Rsquared

```

```{r}
# store results
resdHiAnnoyFitAB['Diff rough', 'RMSE'] <- resultsOutRough$OOB_RMSE
resdHiAnnoyFitAB['Diff rough', 'MAE'] <- resultsOutRough$OOB_MAE
resdHiAnnoyFitAB['Diff rough', 'Rsquared'] <- resultsOutRough$Rsquared
resdHiAnnoyPermImpAB$DiffRough <- resultsOutRough$conditional_permimp

```

###### Plot results

```{r, fig.width=8,fig.height=2}
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutRough.conimp <- arrange(resultsOutRough$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutRough.conimp) + geom_col(aes(x=factor(rownames(resultsOutRough.conimp), levels=rownames(resultsOutRough.conimp)), y=CondPermImp), fill=mycolours[5], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (% highly annoyed)") + ggtitle("dRoughness") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename="PtsABdHiAnnoydRoughConPermimp.svg", width=8, height=2, path=file.path(outFigPath, "svg"))
  unlink("PtsABdHiAnnoydRoughConPermimp.svg")
  
  ggsave(filename="PtsABdHiAnnoydRoughConPermimp.pdf", width=8, height=2, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdHiAnnoydRoughConPermimp.pdf")
}


```

Selected metric

```{r}

dRoughVar <- "dRoughFZ05ExMaxLR"

```

##### dImpulsiveness

###### Set variables

```{r}
# Impulsiveness
iVars <- c(diffVar, eventVar, "dImpulsSHMAvgMaxLR", "dImpulsSHM05ExMaxLR", "dImpulsSHMPowAvgMaxLR")
dVar <- "dHighAnnoyPc"

seeds <- c(418659, 7805, 38475, 65834, 1653)

```

###### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```

Selected hyperparameters

```{r}

ntree <- 1001
mtry <- as.integer(length(iVars)/1.25)

```


###### Run model

Train preliminary model

```{r}

nperm <- 5

resultsOutImpuls <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutImpuls$OOB_RMSE
resultsOutImpuls$OOB_MAE
resultsOutImpuls$Rsquared

```

Train multiple seeds model

```{r}

resultsOutImpuls <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutImpuls$OOB_RMSE
resultsOutImpuls$OOB_MAE
resultsOutImpuls$Rsquared

```

```{r}

# store results
resdHiAnnoyFitAB['Diff impuls', 'RMSE'] <- resultsOutImpuls$OOB_RMSE
resdHiAnnoyFitAB['Diff impuls', 'MAE'] <- resultsOutImpuls$OOB_MAE
resdHiAnnoyFitAB['Diff impuls', 'Rsquared'] <- resultsOutImpuls$Rsquared
resdHiAnnoyPermImpAB$DiffImpuls <- resultsOutImpuls$conditional_permimp

```

###### Plot results

```{r, fig.width=8,fig.height=2}
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutImpuls.conimp <- arrange(resultsOutImpuls$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutImpuls.conimp) + geom_col(aes(x=factor(rownames(resultsOutImpuls.conimp), levels=rownames(resultsOutImpuls.conimp)), y=CondPermImp), fill=mycolours[6], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (% highly annoyed)") + ggtitle("dImpulsiveness") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename="PtsABdHiAnnoydImpulsConPermimp.svg", width=8, height=2, path=file.path(outFigPath, "svg"))
  unlink("PtsABdHiAnnoydImpulsConPermimp.svg")
  
  ggsave(filename="PtsABdHiAnnoydImpulsConPermimp.pdf", width=8, height=2, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdHiAnnoydImpulsConPermimp.pdf")
}

```

Selected metric

```{r}

dImpulsVar <- "dImpulsSHMAvgMaxLR"

```

#### dSQM and loudness comparison

Now the highest importance dSQMs are ranked against each other, controlling for loudness difference.

##### Include dtonal loudness

###### Set variables

```{r}

iVars <- c(diffVar, eventVar, dSharpVar, dTonLdVar, dFluctVar, dRoughVar, dImpulsVar)
dVar <- "dHighAnnoyPc"

seeds <- c(98465, 54163, 6541, 36485, 849675)

```

###### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```

Selected hyperparameters

```{r}

ntree <- 501
mtry <- as.integer(length(iVars)/1.25)

```


###### Run model

Train preliminary model

```{r}

nperm <- 5

resultsOutSQMs1 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSQMs1$OOB_RMSE
resultsOutSQMs1$OOB_MAE
resultsOutSQMs1$Rsquared

```

Train multiple seeds model

```{r}

resultsOutSQMs1 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSQMs1$OOB_RMSE
resultsOutSQMs1$OOB_MAE
resultsOutSQMs1$Rsquared

```

```{r}

# store results
resdHiAnnoyFitAB['Diff SQMs inc tonal loud', 'RMSE'] <- resultsOutSQMs1$OOB_RMSE
resdHiAnnoyFitAB['Diff SQMs inc tonal loud', 'MAE'] <- resultsOutSQMs1$OOB_MAE
resdHiAnnoyFitAB['Diff SQMs inc tonal loud', 'Rsquared'] <- resultsOutSQMs1$Rsquared
resdHiAnnoyPermImpAB$DiffSQMs1 <- resultsOutSQMs1$conditional_permimp

```

###### Plot results

```{r, fig.width=8,fig.height=2.4}
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutSQMs1.conimp <- arrange(resultsOutSQMs1$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutSQMs1.conimp) + geom_col(aes(x=factor(rownames(resultsOutSQMs1.conimp), levels=rownames(resultsOutSQMs1.conimp)), y=CondPermImp), fill=mycolours[7], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (% highly annoyed)") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 50))
pBar

if (saveplots){
  ggsave(filename="PtsABdHiAnnoyDiffSQMsTonLdConPermimp.svg", width=8, height=2.4, path=file.path(outFigPath, "svg"))
  unlink("PtsABdHiAnnoyDiffSQMsTonLdConPermimp.svg")
  
  ggsave(filename="PtsABdHiAnnoyDiffSQMsTonLdConPermimp.pdf", width=8, height=2.4, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdHiAnnoyDiffSQMsTonLdConPermimp.pdf")
}

```

##### Exclude tonal loudness

###### Set variables

```{r}

iVars <- c(diffVar, eventVar, dSharpVar, dTonalVar, dFluctVar, dRoughVar, dImpulsVar)
dVar <- "dHighAnnoyPc"

seeds <- c(49865, 7852, 845961, 410583, 36748)

```

###### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seed=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```

Selected hyperparameters

```{r}

ntree <- 251
mtry <- as.integer(length(iVars)/1.25)

```

###### Run model

Train preliminary model

```{r}

nperm <- 5

resultsOutSQMs2 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSQMs2$OOB_RMSE
resultsOutSQMs2$OOB_MAE
resultsOutSQMs2$Rsquared

```

Train multiple seeds model

```{r}

resultsOutSQMs2 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSQMs2$OOB_RMSE
resultsOutSQMs2$OOB_MAE
resultsOutSQMs2$Rsquared

```

```{r}

# store results
resdHiAnnoyFitAB['Diff SQMs no tonal loud', 'RMSE'] <- resultsOutSQMs2$OOB_RMSE
resdHiAnnoyFitAB['Diff SQMs no tonal loud', 'MAE'] <- resultsOutSQMs2$OOB_MAE
resdHiAnnoyFitAB['Diff SQMs no tonal loud', 'Rsquared'] <- resultsOutSQMs2$Rsquared
resdHiAnnoyPermImpAB$DiffSQMs2 <- resultsOutSQMs2$conditional_permimp

```

###### Plot results

```{r, fig.width=8,fig.height=2.4}
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutSQMs2.conimp <- arrange(resultsOutSQMs2$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutSQMs2.conimp) + geom_col(aes(x=factor(rownames(resultsOutSQMs2.conimp), levels=rownames(resultsOutSQMs2.conimp)), y=CondPermImp), fill=mycolours[7], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (% highly annoyed)") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 50))
pBar

if (saveplots){
  ggsave(filename="PtsABdHiAnnoyDiffSQMsNoTonLdConPermimp.svg", width=8, height=2.4, path=file.path(outFigPath, "svg"))
  unlink("PtsABdHiAnnoyDiffSQMsNoTonLdConPermimp.svg")
  
  ggsave(filename="PtsABdHiAnnoyDiffSQMsNoTonLdConPermimp.pdf", width=8, height=2.4, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdHiAnnoyDiffSQMsNoTonLdConPermimp.pdf")
}

```

### Save the results outputs to file

```{r}

if (savedata){
  utils::write.csv(resdHiAnnoyFitAB, paste(outDataPath, "\\PtsABCRFdHiAnnoyOOBFit.csv", sep=""))
  ii <- 0
  temp = list()
  for (res in resdHiAnnoyPermImpAB){
    ii <- ii + 1
    temp[[ii]] <- as.data.frame(resdHiAnnoyPermImpAB[ii])
    names(temp[[ii]]) <- names(resdHiAnnoyPermImpAB[ii])
  }
  openxlsx::write.xlsx(temp, paste(outDataPath, "\\PtsABCRFdHiAnnoyConPermimp.xlsx",
                                   sep=""),
                       rowNames=TRUE)
}

```


## Parts A&B summary

Summary of results for Parts A & B combined

### With tonal loudness

#### Absolute variables

```{r, fig.width=4, fig.height=4}
# combine the annoyance perm importance results

# convert each result to a tibble with rownames added to a column, renaming the data column to 'dAnnoy' etc.
resdAnnoyMnAbsPermImpTblAB <- as.data.frame(resdAnnoyMnPermImpAB$AbsSQMs1/max(resdAnnoyMnPermImpAB$AbsSQMs1)) |>
  tibble::rownames_to_column(var='Variable')
colnames(resdAnnoyMnAbsPermImpTblAB)[2] <- "dAnnoy"

resdHiAnnoyAbsPermImpTblAB <- as.data.frame(resdHiAnnoyPermImpAB$AbsSQMs1/max(resdHiAnnoyPermImpAB$AbsSQMs1)) |>
  tibble::rownames_to_column(var='Variable')
colnames(resdHiAnnoyAbsPermImpTblAB)[2] <- "dHiAnnoy"

# merge the dataframes
resAbsPermImpTblAB <- list(resdAnnoyMnAbsPermImpTblAB, resdHiAnnoyAbsPermImpTblAB) |>
  purrr::reduce(merge, by = c('Variable'), all = T)

# rename the columns
colnames(resAbsPermImpTblAB)[2:3] <- c("Mean change in annoyance", "%HA | HA' (amb.)")
resAbsPermImpTblAB[is.na(resAbsPermImpTblAB)] <- 0

resAbsAB <- tidyr::pivot_longer(resAbsPermImpTblAB, cols=-Variable, names_to="Outcome", values_to="Imp")

# reorder res tibble, descending by the variable Imp grouped sum and create column with new group order as a factor
resAbsAB <- resAbsAB |> mutate(Variable_sum = sum(Imp), .by=Variable) |> arrange(desc(Variable_sum)) |> group_by(Variable_sum, Variable) |>
   mutate(Order = cur_group_id()) |> mutate(Order = as.factor(Order)) |> arrange(desc(Order))

# Reorder outcome levels
resAbsAB$Outcome <- factor(resAbsAB$Outcome, levels=c("Mean change in annoyance", "%HA | HA' (amb.)"))

# plot res as horizontal bar chart, with Imp as y axis, Variable as x axis, Outcome as fill, and Variable_sum as order, relabel x axis with Variable names
pBar <- ggplot(resAbsAB) + geom_col(aes(fill=Outcome, y=Imp, x=Order), colour='grey35', linewidth=0,  width=0.75, show.legend=TRUE) + labs(x="Variable", y="Normalised conditional variable\npermutation importance") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 1)) + scale_fill_manual(values=mycolours, labels=c(expression(paste(bar(Delta~A))), "%HA | HA' (amb.)")) + scale_x_discrete(labels=unique(rev(resAbsAB$Variable))) + guides(fill=guide_legend(title='Outcome'))
pBar + scale_y_continuous(breaks=seq(0, 1, by=0.5))

if (saveplots){
  ggsave(filename="PtsABcrfAbsSQMsSummary.svg", width=8, height=4, path=file.path(outFigPath, "svg"))
  unlink("PtsABcrfAbsSQMsSummary.svg")

  ggsave(filename="PtsABcrfAbsSQMsSummary.pdf", width=8, height=4, path=file.path(outFigPath, "pdf"))
  unlink("PtsABcrfAbsSQMsSummary.pdf")
}


# plot res as horizontal bar chart, with Imp as y axis, Variable as x axis, Outcome as fill, and Variable_sum as order, relabel x axis with Variable names
pBar <- ggplot(resAbsAB) + geom_col(aes(fill=Outcome, y=Imp, x=Order), colour='grey35', linewidth=0,  width=0.75, show.legend=TRUE) + labs(x="Variable", y="Normalised conditional variable\npermutation importance") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2), legend.position = "top") + coord_flip(ylim=c(0, 1)) + scale_fill_manual(values=mycolours, labels=c(expression(paste(bar(Delta~A))), "%HA | HA' (amb.)")) + scale_x_discrete(labels=unique(rev(resAbsAB$Variable))) + guides(fill=guide_legend(title='Outcome', nrow=2, ncol=1))
pBar + scale_y_continuous(breaks=seq(0, 1, by=0.5))

if (saveplots){
  ggsave(filename="PtsABcrfAbsSQMsSummaryNw.svg", width=4, height=4, path=file.path(outFigPath, "svg"))
  unlink("PtsABcrfAbsSQMsSummary.svg")

  ggsave(filename="PtsABcrfAbsSQMsSummaryNw.pdf", width=4, height=4, path=file.path(outFigPath, "pdf"))
  unlink("PtsABcrfAbsSQMsSummary.pdf")
}


```
#### Difference variables

```{r, fig.width=8, fig.height=4}
# combine the annoyance perm importance results

# convert each result to a tibble with rownames added to a column, renaming the data column to 'dAnnoy' etc.
resdAnnoyMnDiffPermImpTblAB <- as.data.frame(resdAnnoyMnPermImpAB$DiffSQMs1/max(resdAnnoyMnPermImpAB$DiffSQMs1)) |>
  tibble::rownames_to_column(var='Variable')
colnames(resdAnnoyMnDiffPermImpTblAB)[2] <- "dAnnoy"

resdHiAnnoyDiffPermImpTblAB <- as.data.frame(resdHiAnnoyPermImpAB$DiffSQMs1/max(resdHiAnnoyPermImpAB$DiffSQMs1)) |>
  tibble::rownames_to_column(var='Variable')
colnames(resdHiAnnoyDiffPermImpTblAB)[2] <- "dHiAnnoy"

# merge the dataframes
resDiffPermImpTblAB <- list(resdAnnoyMnDiffPermImpTblAB, resdHiAnnoyDiffPermImpTblAB) |>
  purrr::reduce(merge, by = c('Variable'), all = T)

# rename the columns
colnames(resDiffPermImpTblAB)[2:3] <- c("Mean change in annoyance", "%HA | HA' (amb.)")
resDiffPermImpTblAB[is.na(resDiffPermImpTblAB)] <- 0

resDiffAB <- tidyr::pivot_longer(resDiffPermImpTblAB, cols=-Variable, names_to="Outcome", values_to="Imp")

# reorder res tibble, descending by the variable Imp grouped sum and create column with new group order as a factor
resDiffAB <- resDiffAB |> mutate(Variable_sum = sum(Imp), .by=Variable) |> arrange(desc(Variable_sum)) |> group_by(Variable_sum, Variable) |>
   mutate(Order = cur_group_id()) |> mutate(Order = as.factor(Order)) |> arrange(desc(Order))

# Reorder outcome levels
resDiffAB$Outcome <- factor(resDiffAB$Outcome, levels=c("Mean change in annoyance", "%HA | HA' (amb.)"))

# plot res as horizontal bar chart, with Imp as y axis, Variable as x axis, Outcome as fill, and Variable_sum as order, relabel x axis with Variable names
pBar <- ggplot(resDiffAB) + geom_col(aes(fill=Outcome, y=Imp, x=Order), colour='grey35', linewidth=0,  width=0.75, show.legend=TRUE) + labs(x="Variable", y="Normalised conditional variable permutation importance") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2), legend.position = "right") + coord_flip(ylim=c(-0.1, 1.1)) + scale_fill_manual(values=mycolours) + scale_x_discrete(labels=unique(rev(resDiffAB$Variable)))
pBar + scale_y_continuous(breaks=seq(0, 1, by=0.5))

if (saveplots){
  ggsave(filename="PtsABcrfDiffSQMsSummary.svg", width=8, height=3, path=file.path(outFigPath, "svg"))
  unlink("PtsABcrfDiffSQMsSummary.svg")
  
  ggsave(filename="PtsABcrfDiffSQMsSummary.pdf", width=8, height=3, path=file.path(outFigPath, "pdf"))
  unlink("PtsABcrfDiffSQMsSummary.pdf")
}

```

### No tonal loudness

#### Absolute variables

```{r, fig.width=8, fig.height=4}
# combine the annoyance perm importance results

# convert each result to a tibble with rownames added to a column, renaming the data column to 'dAnnoy' etc.
resdAnnoyMnAbsPermImpNoTonLdTblAB <- as.data.frame(resdAnnoyMnPermImpAB$AbsSQMs2/max(resdAnnoyMnPermImpAB$AbsSQMs2)) |>
  tibble::rownames_to_column(var='Variable')
colnames(resdAnnoyMnAbsPermImpNoTonLdTblAB)[2] <- "dAnnoy"

resdHiAnnoyAbsPermImpNoTonLdTblAB <- as.data.frame(resdHiAnnoyPermImpAB$AbsSQMs2/max(resdHiAnnoyPermImpAB$AbsSQMs2)) |>
  tibble::rownames_to_column(var='Variable')
colnames(resdHiAnnoyAbsPermImpNoTonLdTblAB)[2] <- "dHiAnnoy"

# merge the dataframes
resAbsPermImpNoTonLdTblAB <- list(resdAnnoyMnAbsPermImpNoTonLdTblAB, resdHiAnnoyAbsPermImpNoTonLdTblAB) |>
  purrr::reduce(merge, by = c('Variable'), all = T)

# rename the columns
colnames(resAbsPermImpNoTonLdTblAB)[2:3] <- c("Mean change in annoyance", "%HA | HA' (amb.)")
resAbsPermImpNoTonLdTblAB[is.na(resAbsPermImpNoTonLdTblAB)] <- 0

resAbsNoTonLdAB <- tidyr::pivot_longer(resAbsPermImpNoTonLdTblAB, cols=-Variable, names_to="Outcome", values_to="Imp")

# reorder res tibble, descending by the variable Imp grouped sum and create column with new group order as a factor
resAbsNoTonLdAB <- resAbsNoTonLdAB |> mutate(Variable_sum = sum(Imp), .by=Variable) |> arrange(desc(Variable_sum)) |> group_by(Variable_sum, Variable) |>
   mutate(Order = cur_group_id()) |> mutate(Order = as.factor(Order)) |> arrange(desc(Order))

# Reorder outcome levels
resAbsNoTonLdAB$Outcome <- factor(resAbsNoTonLdAB$Outcome, levels=c("Mean change in annoyance", "%HA | HA' (amb.)"))

# plot res as horizontal bar chart, with Imp as y axis, Variable as x axis, Outcome as fill, and Variable_sum as order, relabel x axis with Variable names
pBar <- ggplot(resAbsNoTonLdAB) + geom_col(aes(fill=Outcome, y=Imp, x=Order), colour='grey35', linewidth=0,  width=0.75, show.legend=TRUE) + labs(x="Variable", y="Normalised conditional variable permutation importance") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2), legend.position = "right") + coord_flip(ylim=c(-0.1, 1.1)) + scale_fill_manual(values=mycolours) + scale_x_discrete(labels=unique(rev(resAbsNoTonLdAB$Variable)))
pBar + scale_y_continuous(breaks=seq(0, 1, by=0.5))

if (saveplots){
  ggsave(filename="PtsABcrfAbsSQMsNoTonLdSummary.svg", width=8, height=3, path=file.path(outFigPath, "svg"))
  unlink("PtsABcrfAbsSQMsNoTonLdSummary.svg")
  
  ggsave(filename="PtsABcrfAbsSQMsNoTonLdSummary.pdf", width=8, height=3, path=file.path(outFigPath, "pdf"))
  unlink("PtsABcrfAbsSQMsNoTonLdSummary.pdf")
}

```
#### Difference variables

```{r, fig.width=8, fig.height=4}
# combine the annoyance perm importance results

# convert each result to a tibble with rownames added to a column, renaming the data column to 'dAnnoy' etc.
resdAnnoyMnDiffPermImpNoTonLdTblAB <- as.data.frame(resdAnnoyMnPermImpAB$DiffSQMs2/max(resdAnnoyMnPermImpAB$DiffSQMs2)) |>
  tibble::rownames_to_column(var='Variable')
colnames(resdAnnoyMnDiffPermImpNoTonLdTblAB)[2] <- "dAnnoy"

resdHiAnnoyDiffPermImpNoTonLdTblAB <- as.data.frame(resdHiAnnoyPermImpAB$DiffSQMs2/max(resdHiAnnoyPermImpAB$DiffSQMs2)) |>
  tibble::rownames_to_column(var='Variable')
colnames(resdHiAnnoyDiffPermImpNoTonLdTblAB)[2] <- "dHiAnnoy"

# merge the dataframes
resDiffPermImpNoTonLdTblAB <- list(resdAnnoyMnDiffPermImpNoTonLdTblAB, resdHiAnnoyDiffPermImpNoTonLdTblAB) |>
  purrr::reduce(merge, by = c('Variable'), all = T)

# rename the columns
colnames(resDiffPermImpNoTonLdTblAB)[2:3] <- c("Mean change in annoyance", "%HA | HA' (amb.)")
resDiffPermImpNoTonLdTblAB[is.na(resDiffPermImpNoTonLdTblAB)] <- 0

resDiffNoTonLdAB <- tidyr::pivot_longer(resDiffPermImpNoTonLdTblAB, cols=-Variable, names_to="Outcome", values_to="Imp")

# reorder res tibble, descending by the variable Imp grouped sum and create column with new group order as a factor
resDiffNoTonLdAB <- resDiffNoTonLdAB |> mutate(Variable_sum = sum(Imp), .by=Variable) |> arrange(desc(Variable_sum)) |> group_by(Variable_sum, Variable) |>
   mutate(Order = cur_group_id()) |> mutate(Order = as.factor(Order)) |> arrange(desc(Order))

# Reorder outcome levels
resDiffNoTonLdAB$Outcome <- factor(resDiffNoTonLdAB$Outcome, levels=c("Mean change in annoyance", "%HA | HA' (amb.)"))

# plot res as horizontal bar chart, with Imp as y axis, Variable as x axis, Outcome as fill, and Variable_sum as order, relabel x axis with Variable names
pBar <- ggplot(resDiffNoTonLdAB) + geom_col(aes(fill=Outcome, y=Imp, x=Order), colour='grey35', linewidth=0,  width=0.75, show.legend=TRUE) + labs(x="Variable", y="Normalised conditional variable permutation importance") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2), legend.position = "right") + coord_flip(ylim=c(-0.1, 1.1)) + scale_fill_manual(values=mycolours) + scale_x_discrete(labels=unique(rev(resDiffNoTonLdAB$Variable)))
pBar + scale_y_continuous(breaks=seq(0, 1, by=0.5))

if (saveplots){
  ggsave(filename="PtsABcrfDiffSQMsNoTonLdSummary.svg", width=8, height=3, path=file.path(outFigPath, "svg"))
  unlink("PtsABcrfDiffSQMsNoTonLdSummary.svg")
  
  ggsave(filename="PtsABcrfDiffSQMsNoTonLdSummary.pdf", width=8, height=3, path=file.path(outFigPath, "pdf"))
  unlink("PtsABcrfDiffSQMsNoTonLdSummary.pdf")
}

```

### Save the results outputs to file

```{r}
# Make a list of the summary results
resSummary <- list(resAbsAB, resDiffAB, resAbsNoTonLdAB, resDiffNoTonLdAB)

# Save the results
if (savedata){
  ii <- 0
  temp = list()
  for (res in resSummary){
    ii <- ii + 1
    temp[[ii]] <- data.frame(resSummary[ii])
  }
  openxlsx::write.xlsx(temp, paste(outDataPath, "\\PtsABCRFSummary.xlsx",
                                   sep=""),
                       rowNames=TRUE)
}

```